Tracing the Shadows: Automatic Tracking and Analysis of Crypto Money Laundering via Transaction Semantic Analysis

2026-07-21

AI Summary

This work addresses the limitations of existing anti-money laundering (AML) approaches in interpreting high-level semantics of decentralized finance (DeFi) transactions and accurately tracing illicit cross-chain fund flows. The authors propose AMLGuard, a novel framework that introduces a semantic-aware mechanism to abstract raw transactions into DeFi Semantic Units (DSUs). By integrating static rules with a retrieval-augmented large language model, AMLGuard enables semantic-level comprehension of money laundering activities and supports continuous cross-chain monitoring. Evaluated on 82 real-world cases involving over $1 billion in illicit funds, the framework achieves destination precision rates of 94.4% and 87.6% in single-chain and cross-chain scenarios, respectively, along with address recall rates of 98.4% and 95.8%, significantly outperforming current state-of-the-art methods.

Abstract

With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.

Problem

• crypto money laundering
• DeFi transactions
• semantic complexity
• cross-chain tracking
• illicit fund flow

Innovation

• semantic-aware AML
• DeFi transaction analysis
• retrieval-augmented LLM
• cross-chain tracking
• DeFi Semantic Unit