RISKTAGGER: Evidence-Guided LLM Agent for Post-Incident Forensic Analysis of Money Laundering in Web3
LLM-powered forensic agent automates post-incident money-laundering analysis across fragmented Web3 transaction chains, reducing manual effort in crypto AML investigations.
Summary written by editorial AI · Source link below
arXiv:2510.17848v2 Announce Type: replace Abstract: Cryptocurrency money-laundering forensic analysis after Web3 incidents faces challenges such as fragmented evidence, expanding transaction paths, and cross-chain discontinuity. Existing Web3 AML methods largely rely on manual clues and heuristic or graph-search-based tracing, with outputs limited to lists of suspicious addresses and lacking path-level evidence and verifiable explanations. Directly applying general-purpose large language models
Editorial Analysis
Crypto-related incidents increasingly require cross-chain forensic analysis that manual methods cannot scale; automated LLM agents could accelerate AML response times.
Pilot LLM-assisted forensic analysis for your next crypto-incident tabletop exercise to gauge efficiency gains.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the Research Desk
- Is That Really My X-Ray? Measuring Internet-Exposed DICOM Services in the Presence of Deception20 Jul
- Characterizing Phishing Pages by JavaScript Capabilities20 Jul
- Intentional Electromagnetic Interference Attacks on Facial Recognition20 Jul
- DoSQ: A Cross-Layer Denial of Service Quality Attack by Exploiting Side Channels in 5G NR20 Jul
- Vogls: a Fast Interactive Full-timing Simulator for Pre-silicon Power Side-Channel Analysis20 Jul