AI 中文总结
研究针对加密货币洗钱问题，提出AMLGuard语义感知框架，结合静态规则分析与LLM推理，对复杂DeFi交易语义分析，实现非法资金流追踪，在真实案例评估中取得高精度召回率，有效应对加密货币洗钱。
AI 中文摘要
随着去中心化金融（DeFi）的迅速发展，与加密货币相关的安全事件日益普遍。事件发生后，攻击者通常会迅速转移被盗资产，隐藏非法资金来源并最终将其转换为法定货币。现有反洗钱（AML）方法难以应对DeFi交易的语义复杂性，要么严重依赖低级代币转移，要么进行协议无关的资金流分析，无法捕捉交易的高级意图。本文提出了AMLGuard，一种用于基于账户的区块链的语义感知AML框架。AMLGuard通过对复杂的DeFi交易进行语义分析来追踪已知恶意地址的非法资金流动，实现准确且持续的洗钱追踪。对于复杂交易，AMLGuard将基于规则的静态分析与检索增强的大语言模型（LLM）推理相结合，以推断隐含的DeFi语义，将原始交易数据转换为高级语义表示。此外，对于洗钱意图未明确暴露的跨链交易，AMLGuard解析交易参数并进行论证解析以恢复跨链语义，实现跨账本的无缝追踪。基于推断出的语义，AMLGuard将每个交易抽象为一个DeFi语义单元（DSU）。我们在82个涉及价值超过10亿美元非法资产的真实洗钱案例上评估了AMLGuard的有效性。具体而言，AMLGuard在单链和跨链数据集上分别以94.4%和87.6%的目标精度重建了紧凑的非法资金流拓扑结构，同时实现了98.4%和95.8%的最高地址召回率以及94.1%和93.8%的目标召回率。
英文摘要
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.
