RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912662365929472 |
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| author | Lin, Dan Ding, Yanli Zou, Weipeng Chen, Jiachi Luo, Xiapu Wu, Jiajing Zheng, Zibin |
| author_facet | Lin, Dan Ding, Yanli Zou, Weipeng Chen, Jiachi Luo, Xiapu Wu, Jiajing Zheng, Zibin |
| contents | While the rapid growth of Web3 has driven the development of decentralized finance, user anonymity and cross-chain asset flows make on-chain laundering behaviors more covert and complex. In this context, constructing high-quality anti-money laundering(AML) datasets has become essential for risk-control systems and on-chain forensic analysis, yet current practices still rely heavily on manual efforts with limited efficiency and coverage. In this paper, we introduce RiskTagger, a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3. RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations. RiskTagger implements an end-to-end multi-module agent, integrating a key-clue extractor, a multichain fetcher with a laundering-behavior reasoner, and a data explainer, forming a data annotation pipeline. Experiments on the real case Bybit Hack (with the highest stolen asset value) demonstrate that RiskTagger achieves 100% accuracy in clue extraction, 84.1% consistency with expert judgment, and 90% coverage in explanation generation. Overall, RiskTagger automates laundering behavior annotation while improving transparency and scalability in AML research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors Lin, Dan Ding, Yanli Zou, Weipeng Chen, Jiachi Luo, Xiapu Wu, Jiajing Zheng, Zibin Cryptography and Security Software Engineering While the rapid growth of Web3 has driven the development of decentralized finance, user anonymity and cross-chain asset flows make on-chain laundering behaviors more covert and complex. In this context, constructing high-quality anti-money laundering(AML) datasets has become essential for risk-control systems and on-chain forensic analysis, yet current practices still rely heavily on manual efforts with limited efficiency and coverage. In this paper, we introduce RiskTagger, a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3. RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations. RiskTagger implements an end-to-end multi-module agent, integrating a key-clue extractor, a multichain fetcher with a laundering-behavior reasoner, and a data explainer, forming a data annotation pipeline. Experiments on the real case Bybit Hack (with the highest stolen asset value) demonstrate that RiskTagger achieves 100% accuracy in clue extraction, 84.1% consistency with expert judgment, and 90% coverage in explanation generation. Overall, RiskTagger automates laundering behavior annotation while improving transparency and scalability in AML research. |
| title | RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2510.17848 |