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This paper introduces AMLGuard, a semantic-aware framework designed to enhance anti-money laundering (AML) efforts in decentralized finance (DeFi) by accurately tracking illicit fund flows through complex transactions. By combining static rule-based analysis with retrieval-augmented large language model reasoning, AMLGuard transforms raw transaction data into high-level semantic representations, enabling it to capture the intent behind transactions that traditional methods overlook. Evaluated on 82 real-world laundering cases, AMLGuard achieves impressive metrics, including a destination precision of 94.4% and an address recall of 98.4%, demonstrating its effectiveness in tracing illicit activities across both single-chain and cross-chain environments.
AMLGuard can track illicit funds with a remarkable 98.4% address recall, revolutionizing the fight against crypto money laundering.
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.
