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Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that an unverified contract0x11a4a5 related to galaxyfoxtoken on eth was exploited with a loss of 108eth worth 330k attacker 0xfce19f8f823759b5867ef9a5055a376f20c5e454 httpstcogj8adosfcb posted at 20240510 022451 utc Output: [{'address': '0xFcE19F8f823759b5867ef9a5055A376f20c5E454', 'entity': 'Galaxy For Token exploit (05-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikalert minterest 24hour buying competition join buskonbase now ca 0x128fb70ba0d3501c19df3ae601926854ed591aee capitalize on elon musks support for trump our coin is taking off prizes 1st 80 1m busk 2nd 50 650k busk 3rd 20 350k busk price can go 10x from here Output: [{'address': '0x128Fb70ba0D3501c19df3ae601926854Ed591aeE', 'entity': 'BUSKonBase', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer dumped tons of dym on ethereum for 491 ethworth 116k note that the dym is not the real token of the dymension the price of dym has dropped 10000 dym unknown unknown unknown 0x7e702e868c563b64fe39c146762a2e0cbc89f6c9 httpstcoxtzsjfoab2 posted at 20240207 055516 utc Output: [{'address': '0x7e702e868c563b64fe39c146762a2e0cbc89f6c9', 'entity': 'Dymension', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikinsight we have detected tornado cash deposits from 0x374f4bd39ad211e67e0f8cb8ebf1b4f6ac3059ad on ethereum the fund traced to the withdrawal of 477 eth and 322m brett 2m from ethtrustfund_ gnosissafe on the base chain the project is an exit scam and the x account and website are both down Output: [{'address': '0x374f4BD39aD211e67e0f8Cb8Ebf1b4F6Ac3059aD', 'entity': 'ETH Trust Fund Rug pull (07-2024)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert _woofi on arbitrum was attacked with a loss of 2023 eth worth 72m assets are now sitting in the address 0xb59d29ad the woonetwork sent an onchain message to the exploiter to negotiate a whitehat bounty attacker 0x9961190b258897bca7a12b8f37f415e689d281c4 posted at 20240306 000922 utc Output: [{'address': '0x9961190b258897bca7a12b8f37f415e689d281c4', 'entity': 'WOOFi Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of strk for 844k usdt the price of strk on bnbchain has dropped 10000 note that it is not the real token of starknet strk 0xcc62cf4ee73812a670ad930b9a34ff00a235014f httpstcor9vnvgrsi8 posted at 20240221 022636 utc Output: [{'address': '0xcc62cf4ee73812a670ad930b9a34ff00a235014f', 'entity': 'UNKNOWN', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: a wallet related to the httpstcojqz8wvish6 team deposited 880k ethfi187m to binance 10 minutes ago this wallet received 10m ethfi652m from the httpstcojqz8wvish6 team wallet 3 months ago address unknown unknown 0x57cc1cae9e9567ebdbc9537536916273a2015491 httpstconhlp1torxu Output: [{'address': '0x57CC1CAE9E9567EBDBc9537536916273A2015491', 'entity': 'Binance', 'type': 'UNKNOWN'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: dont send money to unknown 0x521ec1776ddff664e04a7dc6f3c0ad8ca8a988df fake profile and use some algo to take out your oney Output: [{'address': '0x521ec1776ddff664e04a7dc6f3c0ad8ca8a988df', 'entity': 'fake profile', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: in our attempt to warn users of risky tokens as early as possible hashdit will continually share our consolidated list regularly 0x6dc8d8748f538a8458acc69d4b66b26a2168169c 0xc81688968bd1e8da313b8f2936852ec4307cd17f 0x766e09665a8128d9f7fd9d90bcd3d9cdc50b067f 0x8f174e364d8ed26a30006e56c756317de1860daf 0x09c704c1eb9245af48f058878e72129557a10f04 0x9512ff466483408293ca277e0ede2b693ce7f005 0xae3e29026bb7d0a1f2b5393823382f652b57f86c 0x2812269fd1005901697040c77a242fdb19de0257 0xd31ccd619a9c7a6e9d393645fe9f26b975f0da5e 0x2b0f4769b05e2084e57de3629d076ea5e8369c7a 0xd06f8d890028098d3f86aca6cd2a4102e25d9f8f educate yourself about the risks and always do your own research dyor when diving into new ventures psa do reach out to us on tg or our mail to dispute any disagreements we will be glad to resolve them thank you and stay vigilant Output: [{'address': '0x6dc8d8748f538a8458acc69d4b66b26a2168169c', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xc81688968bd1e8da313b8f2936852ec4307cd17f', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x766e09665a8128d9f7fd9d90bcd3d9cdc50b067f', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x8f174e364d8ed26a30006e56c756317de1860daf', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x09c704c1eb9245af48f058878e72129557a10f04', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x9512ff466483408293ca277e0ede2b693ce7f005', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xae3e29026bb7d0a1f2b5393823382f652b57f86c', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x2812269fd1005901697040c77a242fdb19de0257', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xd31ccd619a9c7a6e9d393645fe9f26b975f0da5e', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x2b0f4769b05e2084e57de3629d076ea5e8369c7a', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xd06f8d890028098d3f86aca6cd2a4102e25d9f8f', 'entity': 'UNKNOWN', 'type': 'Suspicious'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert magpieprotocol announced that all users with any approval and funds in their wallets should remove their approvals to magpie contracts now revoke your approvals with httpstcob9cvifolj6 magpie contract addressesto be revoked ethereum 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon 0xcf32c5bb41f7a302298a2d2072155800871baad3 bsc 0xcf32c5bb41f7a302298a2d2072155800871baad3 avalanche 0x746b0ca3762e229d4dcbd22b4a10906aa788d396 arbitrum 0xcf32c5bb41f7a302298a2d2072155800871baad3 optimism 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon zkevm 0x59b37ed62599f3d2f9a593be0153ef08702cb370 base 0x6a1431bb23e08e3209dae3130b441863855fc14b zksync 0x5fe556bcf5fc7db6e075ca6f4cd4f8bbee2a3e54 blast 0x956df8424b556f0076e8abf5481605f5a791cc7f blast 0x956df8424b556f0076e8abf5481605f5a791cc7f posted at 20240423 103201 utc Output: [{'address': '0x59b37ed62599f3d2f9a593be0153ef08702cb370', 'entity': 'Magpie Contract', 'type': 'Hack'}, {'address': '0xcf32c5bb41f7a302298a2d2072155800871baad3', 'entity': 'Magpie Protocol Exploit (04-2024)', 'type': 'Hack'}, {'address': '0x746b0ca3762e229d4dcbd22b4a10906aa788d396', 'entity': 'Magpie Protocol Exploit (04-2024)', 'type': 'Hack'}, {'address': '0x6a1431bb23e08e3209dae3130b441863855fc14b', 'entity': 'Magpie Protocol Exploit (04-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull another fake aevo rugpull the aevo deployer dumped tons of aevo on bnbchain for 612k its price has dropped 98 aevo 0xa521273d4d20ab8c1c3531a96443ab6bc5e74427 httpstcoxftk0xl5zk posted at 20240308 131309 utc Output: [{'address': '0xa521273d4d20ab8c1c3531a96443ab6bc5e74427', 'entity': '$AEVO deployer', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: as the market declines the whale who is long eth is on the verge of liquidation he deposited 12734 eth40m to compound and borrowed 314m stablecoins with a health rate of only 106 when the price of eth drops to 2984 he will be liquidated address unknown 0xc26b5977c42c4fa2dd41750f8658f6bd2b67869c Output: [{'address': '0xC26b5977C42C4fa2DD41750F8658f6Bd2B67869C', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of ethfi on ethereum for 122 eth worth 418k note that it is not the real token of the liquid restaking protocol etherfi its price has dropped 10000 scammer 0x4fce9b773c32457990e4311a8e30a1461ae3cff7 httpstcobe3liv7ljo posted at 20240319 022134 utc Output: [{'address': '0x4FCe9b773C32457990e4311A8E30a1461ae3CFF7', 'entity': 'ETHFI Token Rug pull (2024-03)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert bazaar on blast was attacked with a loss of 392 eth worth 14m due to an insufficient access control attacker 0x3cf5b87726af770c94494e886d2a69c42a203884 392 eth now sit at below wallets 0xfb72b2b32740de44488db791d83125ce39a53d91 0xde4d48f64687888d112b653c8fab4f0f67c37dbb posted at 20240611 031417 utc Output: [{'address': '0xfb72b2b32740de44488db791d83125ce39a53d91', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}, {'address': '0xde4d48f64687888d112b653c8fab4f0f67c37dbb', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}, {'address': '0x3cf5B87726Af770c94494E886d2A69c42A203884', 'entity': 'Baazar on Blast Exploit (06-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: the attacker took control of the deployer address on blast and withdrew various assets worth 13m to unknown 0x895a80371fc0e6987e27ddc7aa0e851bc3538ea8 bridged the fund to ethereum unknown 0xd30ebc0a9acda91d383675eaab3ff24f06d07ece and deposit them into tornado cash Output: [{'address': '0xd30eBC0a9AcdA91d383675EAAB3ff24f06d07eCE', 'entity': 'Tornado Cash', 'type': 'Darkweb'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that yon on bnbchain was attacked with a loss of 190 bnb worth 118k due to insufficient access control attacker 0xbc9271380b4589e08af015069eee7bb57869cf06 httpstcono0m3s8sig posted at 20240522 102009 utc Output: [{'address': '0xbc9271380b4589e08af015069eee7bb57869cf06', 'entity': 'YESorNO: YON Token Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: over the past 15 months one person has created 114 meme coin scams each time stolen funds from the scam are sent to the exact same deposit address 0x739c58807b99cb274f6fd96b10194202b8eefb47 httpstcouwvaig9wgg Output: [{'address': '0x739c58807B99Cb274f6FD96B10194202b8EEfB47', 'entity': 'Scam Meme Coins', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: zachxbt kchimaev don fade wolfskull_erc wolfskull a menacing creation by matt furie in his book indviscosity ca unknown 0x7022fe5fedbd54b40fdc52be30c1c578fb55c2bf httpstcoe0360l65tf Output: [{'address': '0x7022fe5fedbd54b40fdc52be30c1c578fb55c2bf', 'entity': 'Wolfskull_ERC', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert due to the dumpershido exploit 202403 shido exploit 202403 0x1982358c84da9d0b4b96fc9e8564d132f7d0041f who is the owner of the staking contractunknown unknown 0xcda954a0c574d8c408f0b8c89a2b367d6a2d3354 as the above screenshot mentioned the owner upgraded the staking contract withdrew and dumped the shido token so it is probably a rug pull upgrade tx 0x5d4056cdf40d09a6715fd0f26895d0c60038899b45620f0a6a402c4cd425b672 withdraw tx 0xed3000ddd8b4feb0902107f97a91815ecee8d7ccb57de9a9dbc50a4c07593cb3 one dump tx 0x6a27674e9916064eebbd742ab2829e5cab210d86cb1331b5fb29ef12aa16f8fc posted at 20240229 061813 utc Output: [{'address': '0xcda954A0C574d8C408F0b8c89a2B367d6A2D3354', 'entity': 'staking contract', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lookonchain machi big brother is below thats the remedy for recover fr rps on solana j6tazgeusvortjjppsfpe51iimkejyuxrrehffld3iss httpstco7z7lwupq8r Output: [{'address': 'J6taZgEUsvortJJPPSfpe51iiMKeJYUXRreHffLD3isS', 'entity': 'Machi Big Brother', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that an unverified contract0x00dd46 on optimism was attacked due to insufficient parameter check resulting in users who approved the contract losing funds please revoke approvals from the vulnerable contract unknown unknown 0x00dd464dba9fc0c20c4cc4d470e8bf965788c150 posted at 20240513 024736 utc Output: [{'address': '0x00dd464dba9fc0c20c4cc4d470e8bf965788c150', 'entity': 'Optimism', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashdit 0x78705c05cac2624eb4ad6194f3f6420630e2bca4 bu adres tokenlerimi ald kayet ettim halen tokenlerim bu czdan da ne yapmam gerekiyor ltfen yardm edin Output: [{'address': '0x78705C05caC2624EB4AD6194F3F6420630E2bCA4', 'entity': 'Phishing Scam reported by Cyvers ', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert due to the dumper0x1982358c84da9d0b4b96fc9e8564d132f7d0041f who is the owner of the staking contract0xcda954a0c574d8c408f0b8c89a2b367d6a2d3354 as the above screenshot mentioned the owner upgraded the staking contract withdrew and dumped the shido token so it is probably a rug pull upgrade tx 0x5d4056cdf40d09a6715fd0f26895d0c60038899b45620f0a6a402c4cd425b672 withdraw tx 0xed3000ddd8b4feb0902107f97a91815ecee8d7ccb57de9a9dbc50a4c07593cb3 one dump tx 0x6a27674e9916064eebbd742ab2829e5cab210d86cb1331b5fb29ef12aa16f8fc posted at 20240229 061813 utc Output: [{'address': '0x1982358C84DA9D0b4B96FC9e8564d132f7d0041F', 'entity': 'Shido Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: when germany dumps we pump the fun say hello to jeetmany the latest and greatest memecoin on the solana blockchain thats here to turn market chaos into comedy gold httpspumpfun7jtdrvnkmgcshrfx8pbewf9mgqdxchdwznx2kjl8lmychttpspumpfun7jtdrvnkmgcshrfx8pbewf9mgqdxchdwznx2kjl8lmyc Output: [{'address': '7jTdRVNkMGCsHRfx8pBewf9mGqDXCHDWzNX2kjL8Lmyc', 'entity': 'JEETMANY', 'type': 'ICO'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 4123131719139 omni on bnbchain for 270k note that it is not real token of the omninetwork its price has dropped 10000 scammer 0xddc4dd1fc88748b0f38d28318e6d711ee38b1ecb httpstcoevkevhh09w posted at 20240415 021304 utc Output: [{'address': '0xDDC4DD1FC88748b0f38d28318e6d711ee38B1ECb', 'entity': 'OMNI Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: in our attempt to warn users of risky tokens as early as possible hashdit will continually share our consolidated list regularly 0xbcf621d79f1da2136f732ac9c0fe7af2268d2b79 0x8bec5226504399997d1efef15cd05623b3cf8888 0x7c51cc9485758883b45e544e4a62daa2c6d962d8 0xc298d75509943156f1b43f8ba9f484827a22c2c2 0xefa4cda666c6a1c03a38e4885266b019dec57662 educate yourself about the risks and always do your own research dyor when diving into new ventures psa do reach out to us on tg or our mail to dispute any disagreements we will be glad to resolve them thank you and stay vigilant update Output: [{'address': '0xc298d75509943156f1b43f8ba9f484827a22c2c2', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}, {'address': '0xefa4cda666c6a1c03a38e4885266b019dec57662', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}, {'address': '0x7c51cc9485758883b45e544e4a62daa2c6d962d8', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert prismafi a copycat attacker attacked the vulnerable contract resulting a loss of 118 wsteth 489k which make the total loss of this vulnerability reach to 99m attacker 0x4148310fe4544e82f176570c6c7b649290a90e17 httpstcody44sxjy9t posted at 20240328 125548 utc Output: [{'address': '0x4148310fe4544e82f176570c6c7b649290a90e17', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: 6 shortly after downloading the victim crypto assets were transferred out from their wallets funds were then transferred through multiple intermediary addresses and deposited to exchanges theft address 0x77afc774c38d6a712e1a1f5ea7c88fe14bfa10f6 httpstcom2e2xh5ats Output: [{'address': '0x77aFC774c38D6A712e1A1F5Ea7c88Fe14BFA10F6', 'entity': 'Reported by the CDA ', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: skuullls thanks evm zachxbt legal fund donation address 0x9d727911b54c455b0071a7b682fcf4bc444b5596 sol investigationssol Output: [{'address': '0x9D727911B54C455B0071A7B682FcF4Bc444B5596', 'entity': 'ZachXBT Legal Fund Donation Address', 'type': 'Wallet'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 81948555159892 andy on eth for 643 eth worth 221k its price has dropped 10000 scammer 0x676d8edf0307979185181a71503a5a2bd13322d2 httpstcolafplfjfsy posted at 20240408 013026 utc Output: [{'address': '0x676D8EdF0307979185181A71503a5a2BD13322d2', 'entity': 'ANDY Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that tgbs on bnbchain was attacked with a loss of 366 bnb worth 150k attacker 0xff1db040e4f2a44305e28f8de728dabff58f01e1 httpstcoyt51ile8t8 posted at 20240306 081838 utc Output: [{'address': '0xff1db040e4f2a44305e28f8de728dabff58f01e1', 'entity': 'TGBS Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that satx on bnbchain was attacked with a loss of 50 bnb worth 264k due to a logic flaw in the _transfer function the _transfer function first calls the destroypooltoken function that destory the satx in the pair and syncs the pair if the to address is the pair then the function invokes the super_transfer function to increase the satx amount of the lp pair and do not sync the pair it results in that the hacker to steal the extra satx just added of the lp pair by calling the skim function attacker 0xbef24b94c205999ea17d2ae4941ce849c9114bfd posted at 20240417 093334 utc Output: [{'address': '0xBEF24B94C205999ea17d2ae4941cE849C9114bfd', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer dumped tons of zeta on bnbchain for 82k after minted tons of zeta note that the zeta is not the real token of the zetachain the price of zeta has dropped 10000 zeta 0x72dc90812516f8c02121e515aba407497c91a04d httpstcovg0dvdjjfg posted at 20240202 033303 utc Output: [{'address': '0x72dc90812516f8c02121e515aba407497c91a04d', 'entity': '$ZETA', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that an attack on bnbchain on users who approved an closedsource contract0x389a9a with a loss of 46k please revoke approvals from the contract unknown 0x389a9ae29fbe53cca7bc8b7a4d9d0a04078e1c24 attacker phishing scam 229 0x123fa25c574bb3158ecf6515595932a92a1da510 posted at 20240428 024643 utc Output: [{'address': '0x389a9ae29fbe53cca7bc8b7a4d9d0a04078e1c24', 'entity': 'closed-source contract', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert magpieprotocol announced that all users with any approval and funds in their wallets should remove their approvals to magpie contracts now revoke your approvals with httpstcob9cvifolj6 magpie contract addressesto be revoked ethereum magpie protocol exploit 042024 magpie protocol exploit 042024 magpie protocol exploit 042024 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon 0xcf32c5bb41f7a302298a2d2072155800871baad3 bsc 0xcf32c5bb41f7a302298a2d2072155800871baad3 avalanche magpie protocol exploit 042024 magpie protocol exploit 042024 magpie protocol exploit 042024 0x746b0ca3762e229d4dcbd22b4a10906aa788d396 arbitrum magpie protocol exploit 042024 magpie protocol exploit 042024 0xcf32c5bb41f7a302298a2d2072155800871baad3 optimism 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon zkevm unknown unknown unknown 0x59b37ed62599f3d2f9a593be0153ef08702cb370 base magpie protocol exploit 042024 magpie protocol exploit 042024 magpie protocol exploit 042024 0x6a1431bb23e08e3209dae3130b441863855fc14b zksync unknown unknown unknown 0x5fe556bcf5fc7db6e075ca6f4cd4f8bbee2a3e54 blast unknown unknown unknown 0x956df8424b556f0076e8abf5481605f5a791cc7f blast 0x956df8424b556f0076e8abf5481605f5a791cc7f posted at 20240423 103201 utc Output: [{'address': '0x956df8424b556f0076e8abf5481605f5a791cc7f', 'entity': 'magpie protocol', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 1769800761000000 lava on eth for 899 eth worth 285k its price of has dropped 10000 scammer 0x09210aa4c8ceaa054834446d7c09a4c6ffe5f9f2 httpstcopkdluuaqv7 posted at 20240430 021146 utc Output: [{'address': '0x09210AA4C8ceaa054834446d7C09A4C6FFe5F9f2', 'entity': 'Rugpull Lava dumper', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: zachxbt please community help me if their any way zachxbt label thes my mostly funds were moved from lineabuild chain and scroll_zkp chain to the scammers addresses these 2 are scammer addresses 0xcafb167eb32806154006690eaafd79a33bcba85a 0x44d5af27faedb838141b58dd91167c1081b8000d httpstcozgphkcng2w Output: [{'address': '0xcafb167eb32806154006690eaafd79a33bcba85a', 'entity': 'UNKNOWN', 'type': 'Scam'}, {'address': '0x44d5af27faedb838141b58dd91167c1081b8000d', 'entity': 'UNKNOWN', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: following the release of our research linking huione guarantee and huione pay to the laundering of proceeds of online scams tether has blacklisted a tron address belonging to huione pay freezing 296 million tnvakwqzau7xl9bcnvlmf9kseqkwes4ug8 httpstcowvng6jb4rk Output: [{'address': 'TNVaKWQzau7xL9bcnvLmF9KSEQkWEs4Ug8', 'entity': 'Huionepay.com.kh', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert japans cryptocurrency exchange dmm_bitcoin was attacked with a loss of 45029 btc 300 million attacker 1b6rjrfjtxwey36scs5zofgmmdv2kdzw7p httpstcolgmzcvnkd6 posted at 20240531 161918 utc Output: [{'address': '1B6rJRfjTXwEy36SCs5zofGMmdv2kdZw7P', 'entity': 'DMM Bitcoin Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that yiedlai on bnbchain was attacked multiple times with a loss of 160k attacker 0x322696471792440499b1979e0a440491e870667a httpstcokafvpshug5 posted at 20240424 023748 utc Output: [{'address': '0x322696471792440499B1979E0a440491E870667a', 'entity': 'Yiedlai Protocol Exploit (04-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: community alert phishing emails are currently being sent out that appear to be from cointelegraph wallet connect token terminal and defi team emails 580k has been stolen so far 0xe7d13137923142a0424771e1778865b88752b3c7 httpstcoxon65hxoyh Output: [{'address': 'Phishing Scam #147 Phishing Scam #147 0xe7D13137923142A0424771E1778865b88752B3c7', 'entity': 'Phishing Scam', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that liquiditytokens on bnbchain was attacked with a loss of 160k attacker 0x6951eb8a4a1dab360f2230fb654551335d560ec0 httpstcoh6jhilouhr posted at 20240531 110340 utc Output: [{'address': '0x6951EB8a4A1DAb360F2230Fb654551335d560ec0', 'entity': 'Liquidity Tokens Exploit (06-2024)\n', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that stm on bnbchain was under a price manipulation attack with a loss of 13k attacker 0x40a82dfdbf01630ea87a0372cf95fa8636fcad89 httpstcouilrjpf3jw posted at 20240607 041154 utc Output: [{'address': '0x40a82dfdbf01630ea87a0372cf95fa8636fcad89', 'entity': 'STM manipulation attakck \n', 'type': 'Hack'}]