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import os
import openai
import json
import rdflib
class ExampleGenerator:
def __init__(self):
self.ontologies = {}
self.ontology_files = []
self.rules = {}
self.description = None
def add_ontology(self, onto):
if onto in self.ontology_files:
raise ValueError("Ontology file already exists.")
else:
onto_data = self.get_ontology_file(onto)
if onto_data:
self.ontology_files.append(onto)
self.ontologies[onto] = self.get_ontology_file(onto)
self.rules[onto] = self.generate_rules(onto)
else:
raise ValueError("Ontology file error.")
def get_ontology_file(self,filename):
text = ""
if os.path.isfile(filename):
with open(filename,'r') as f:
text = f.read()
f.close()
return text
else:
raise ValueError("Invalid filename.")
def ChatGPTTextSplitter(self,text):
"""Splits text in smaller subblocks to feed to the LLM"""
prompt = f"""The total length of content that I want to send you is too large to send in only one piece.
For sending you that content, I will follow this rule:
[START PART 1/10]
this is the content of the part 1 out of 10 in total
[END PART 1/10]
Then you just answer: "Instructions Sent."
And when I tell you "ALL PARTS SENT", then you can continue processing the data and answering my requests.
"""
if type(text) == str:
textsize = 12000
blocksize = int(len(text) / textsize)
if blocksize > 0:
yield prompt
for b in range(1,blocksize+1):
if b < blocksize+1:
prompt = f"""Do not answer yet. This is just another part of the text I want to send you. Just receive and acknowledge as "Part {b}/{blocksize} received" and wait for the next part.
[START PART {b}/{blocksize}]
{text[(b-1)*textsize:b*textsize]}
[END PART {b}/{blocksize}]
Remember not answering yet. Just acknowledge you received this part with the message "Part {b}/{blocksize} received" and wait for the next part.
"""
yield prompt
else:
prompt = f"""
[START PART {b}/{blocksize}]
{text[(b-1)*textsize:b*textsize]}
[END PART {b}/{blocksize}]
ALL PARTS SENT. Now you can continue processing the request.
"""
yield prompt
else:
yield text
elif type(text) == list:
yield prompt
for n,block in enumerate(text):
if n+1 < len(text):
prompt = f"""Do not answer yet. This is just another part of the text I want to send you. Just receive and acknowledge as "Part {n+1}/{len(text)} received" and wait for the next part.
[START PART {n+1}/{len(text)}]
{text[n]}
[END PART {n+1}/{len(text)}]
Remember not answering yet. Just acknowledge you received this part with the message "Part {n+1}/{len(text)} received" and wait for the next part.
"""
yield prompt
else:
prompt = f"""
[START PART {n+1}/{len(text)}]
{text[n]}
[END PART {n+1}/{len(text)}]
ALL PARTS SENT. Now you can continue processing the request.
"""
yield prompt
def send_ontology(self):
ontology = ""
if len(self.ontologies) > 0:
for k,v in self.ontologies.items():
ontology+=v+"\n"
print("Sending Ontology in Parts")
for i in self.ChatGPTTextSplitter(ontology):
print(self.llm_api(i))
else:
raise ValueError("No loaded ontology to send.")
def llm_api(self,prompt,model="gpt-3.5-turbo"):
messages = [{
"role":"user",
"content":prompt
}]
res = openai.ChatCompletion.create(model=model,messages=messages,temperature=0)
return res.choices[0].message['content']
def generate_rule(self,onto=None):
"""Raw rule string of AEO."""
v = """These are the components that construct the plan:
Fake personas - first and last name
People can have one of the following DeceptionRoles:
adversary
defender
Identities can take one of the following DeceptionActions:
engagement:Access - subject is an identity, predicate is an object
engagement:Alert - subject is an identity, predicate is a human identity
engagement:Beacon - subject is non-human identities, services, or tools, predicate can be a server, service, tool or an identity
engagement:Deploy - subject is human or agent, the predicate must be a DeceptionObject
engagement:Obfuscate
engagement:Respond
There can be one or more DeceptionObjects:
engagement:Honeypot
engagement:Honeytoken
engagement:Breadcrumb
engagement:BreadcrumbTrail - this is a set of breadcrumbs
engagement:LureObject
engagement:HoneyObject
engagement:Decoy
engagement:DataSource
A defender that performs an a DeceptionAction has at least one of the following DefenderObjectives
objective:Reconnaissance
objective:Affect
objective:Collect
objective:Detect
objective:Direct
objective:Disrupt
objective:Elicit
objective:Expose
objective:Motivate
objective:Plan
objective:Prepare
objective:Prevent
objective:Reassure
objective:Analyze
objective:Deny
objective:ElicitBehavior
objective:Lure
objective:TimeSink
objective:Track
objective:Trap
An adversary that performs an a DeceptionAction has at least one of the following AdversaryObjectives
objective:CommandAndControl
objective:CredentialAccess
objective:DevelopResource
objective:Discover
objective:EscalatePrivilege
objective:Evade
objective:Execute
objective:Exfilitrate
objective:GainInitialAccess
objective:Impact
objective:MoveLaterally
objective:Persist
objective:Reconnaissance
Generate the plan in the following structure:
print "Use one engagement:Narrative"
print "Use one engagement:Storyline"
print "Use the following people:"
Enumerate each person's name and DeceptionRole
Each planned event is centered around a DeceptionAction taken by some identity or person with a DeceptionRole onto a DeceptionObject or Object. Enumerate each planned events where each planned event has a short description and number starting with "Planned Event 1". Describe which person deploys a DeceptionObject, what DeceptionAction they used and what the DefenderObjective or AdversaryObjective of the action or deployed DeceptionObject. The last planned event should conclude that an defender has been alerted that the DeceptionObject was accessed by the adversary.
Remember to use only given DefenderObjectives, AdversaryObjectives, DeceptionObjects, DeceptionActions, and DeceptionRoles. Do not use any other objectives, objects, actions, or roles other than what is provided. If a person uses an action not from DeceptionActions, then the action is "uco-core:Action" with the name of action.
"""
return v
def generate_json_rule(self,onto=None):
"""Raw rule string of AEO."""
v = """Remember make a json-ld format example that only uses classes and properties terms from Adversary Engagement Ontology, Unified Cyber Ontology.
Each engagement:Narrative has property:
engagement:hasStoryline connects to an engagement:Storyline
Each engagement:Storyline has property:
engagement:hasEvent connects to a uco-types:Thread
Each uco-types:Thread has properties:
co:element contains all engagement:PlannedEvents
co:item contains all uco-types:ThreadItem one each for each engagement:PlannedEvent.
co:size
uco-types:threadOriginItem is the uco-types:ThreadItem for the first engagement:PlannedEvent
uco-types:threadTerminalItem is the uco-types:ThreadItem for the last engagement:PlannedEvent
Each co:size has properties:
@type as xsd:nonNegativeInteger
@value which is the number of uco-types:ThreadItem
Each uco-types:ThreadItem has property:
co:itemContent is the engagement:PlannedEvent
optional uco-types:threadNextItem is the next uco-types:ThreadItem for the next engagement:PlannedEvent if there is one,
optional uco-types:threadPreviousItem is the previous uco-types:ThreadItem for the previous engagement:PlannedEvent if there is one
Each engagement:PlannedEvent has property:
engagement:eventContext connects to one engagement action has property @type one of the following:
engagement:Access
engagement:Alert
engagement:Beacon
engagement:Deploy
engagement:Obfuscate
engagement:Respond
Each engagement action has properties:
@type is the action
uco-core:performer
uco-core:object connects to one of the following engagement deception object denoted as "EDO" objects:
engagement:Honeypot
engagement:Honeytoken
engagement:Breadcrumb
engagement:BreadcrumbTrail
engagement:LureObject
engagement:HoneyObject
engagement:Decoy
engagement:DataSource
Each "EDO" object has properties:
engagement:hasCharacterization connects to a uco-core:UcoObject
objective:hasObjective with @type objective:Objective and @id with one of the following instances:
objective:CommandAndControl
objective:CredentialAccess
objective:DevelopResource
objective:Discover
objective:EscalatePrivilege
objective:Evade
objective:Execute
objective:Exfilitrate
objective:GainInitialAccess
objective:Impact
objective:MoveLaterally
objective:Persist
objective:Reconnaissance
objective:Affect
objective:Collect
objective:Detect
objective:Direct
objective:Disrupt
objective:Elicit
objective:Expose
objective:Motivate
objective:Plan
objective:Prepare
objective:Prevent
objective:Reassure
objective:Analyze
objective:Deny
objective:ElicitBehavior
objective:Lure
objective:TimeSink
objective:Track
objective:Trap
uco-core:name is the objective
All people have property:
@type is uco-identity:Person
uco-core:hasFacet that connects to one of the following:
uco-identity:SimpleNameFacet which has the property:
uco-identity:familyName
uco-identity:givenName
Each uco-core:Role has properties:
@id is the role
uco-core:name is the role
Each uco-core:Role there is a uco-core:Relationship with properties:
uco-core:kindofRelationship is "has_Role"
uco-core:source connects to the person who has the role
uco-core:target connects to uco-core:Role
Each engagement:BreadcrumbTrail has property:
engagement:hasBreadcrumb connects to uco-types:Thread
This uco-types:Thread has property:
co:element contains all engagement:Breadcrumb that belong to this engagement:BreadcrumbTrail
co:item contains all uco-types:ThreadItem one each for each engagement:Breadcrumb
co:size
uco-types:threadOriginItem is the uco-types:ThreadItem for the first engagement:Breadcrumb belonging to this engagement:BreadcrumbTrail
uco-types:threadTerminalItem is the uco-types:ThreadItem for the last engagement:Breadcrumb belonging to this engagement:BreadcrumbTrail
Each engagement:Breadcrumb has the properties:
engagement:hasCharacterization which connects to a uco-core:UcoObject with the property:
uco-core:description which describes the object characterizing the breadcrumb
All classes must include property:
@type is the class
@id is a unique identifier
If namespace "engagement" prefix is used then https://ontology.adversaryengagement.org/ae/engagement#
If namespace "objective" prefix is used then https://ontology.adversaryengagement.org/ae/objective#
If namespace "role" prefix is used then https://ontology.adversaryengagement.org/ae/role#
If namespace "identity" prefix is used then https://ontology.adversaryengagement.org/ae/identity#
If namespace "uco-core" prefix is used then https://ontology.unifiedcyberontology.org/uco/core#
If namespace "uco-types" prefix is used then https://ontology.unifiedcyberontology.org/uco/types#
If namespace "uco-role" prefix is used then https://ontology.unifiedcyberontology.org/uco/role#
"""
return v
def generate_continue(self):
v = """
continue
"""
return v
def raw_prompt(self,description,jsn=True):
def run(val,jsn):
if jsn:
prompt = f"""Give me a full json-ld format example for the following scenario:
{description}
{"".join(val)}
"""
else:
prompt = f"""
{"".join(val)}
{description}
"""
for i in self.ChatGPTTextSplitter(prompt):
res = self.llm_api(i)
return res
if not jsn:
res_val = run(self.generate_rule(),jsn)
return res_val
else:
res_val = run(self.generate_json_rule(),jsn)
try:
val = json.loads(res_val)
return val
except:
#the response was cut off, prompt for the continuation.
data = []
data.append(res_val)
while True:
res = self.llm_api(self.generate_continue())
data.append(res)
try:
full = "".join(data)
return json.loads(full)
except:
pass
return None
def get_ns(self,string):
return string.split(":")[0]
def auto_generate(self,planSize:int=3,keywords:str="",nkeywords:str=""):
p = f"Generate a deception plan with {planSize} PlannedEvents."
if keywords:
p+= f"The plan must include specifically {keywords}."
if nkeywords:
p+= f"Do not include {nkeywords}."
#deception plan description
e = self.prompt(p,jsn=False)
self.description = e
#deception plan converted into json
w = self.prompt(e,jsn=True)
self.json = w
return self.json
def generate_personas(self):
if not self.description:
raise ValueError("Run auto_generate() or prompt() before attempting to generate persona profiles.")
description =f"""{self.description}
Generate a believable, detailed, and professional description of each person as if they had a LinkedIn profile with the following configurations:
Name
Occupation and occupation job title
Gender
Random configuration from the big five personality traits that fits their occupation and 1 of the 16 Myer-Briggs personality classifications. Do not disclose their configuration or their personality type, write as if they wrote an introduction about themselves and their accomplishments.
Education history including graduation year, school, degree
Job history with at least 10 active years
Rewards that highlight the factitious character's strength
A schedule of their activities and active behaviors at work
"""
w = self.prompt(description,jsn=False)
self.persona_descriptions = w
return w
def prompt(self,description,jsn=True):
if not self.description:
self.description = description
if not jsn:
res = self.raw_prompt(description,jsn=jsn)
else:
res = self.raw_prompt(description)
#include only relevent namespaces
prefixes = []
def is_nested(LIST):
if type(LIST) == list:
for JSON in LIST:
for key in JSON.keys():
if type(JSON[key]) == dict:
is_nested(JSON[key])
if '@type' in JSON.keys():
prefixes.append(self.get_ns(JSON['@type']))
else:
JSON = LIST
for key in JSON.keys():
if type(JSON[key]) == dict:
is_nested(JSON[key])
if '@type' in JSON.keys():
prefixes.append(self.get_ns(JSON['@type']))
is_nested(res['@graph'])
prefixes = set(prefixes)
new_prefixes = {}
for prefix in prefixes:
if prefix in res['@context']:
new_prefixes[prefix] = res['@context'][prefix]
res['@context'] = new_prefixes
return res |