{"ID":2856718,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17848","arxiv_id":"2510.17848","title":"RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors","abstract":"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.","short_abstract":"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-c...","url_abs":"https://arxiv.org/abs/2510.17848","url_pdf":"https://arxiv.org/pdf/2510.17848v1","authors":"[\"Dan Lin\",\"Yanli Ding\",\"Weipeng Zou\",\"Jiachi Chen\",\"Xiapu Luo\",\"Jiajing Wu\",\"Zibin Zheng\"]","published":"2025-10-12T08:54:28Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}