Ethereum tracing tool reveals complex money laundering methods
Tracing Money Laundering Across DeFi, Bridges, and Mixers on Ethereum
Cryptography and Security
Summary
Bad actors use complex paths on Ethereum to hide illegally gained money, mixing services and crossing blockchains. The authors created FPTracer, a new method that follows the flow of money more clearly through complicated transactions than previous tools. FPTracer also spots when money moves across chains and what happens after it goes through mixers. Their approach was tested on real cases and found many hidden pathways criminals used, including ones not known before.
What this means in practice
- •For blockchain compliance teams: Detect complex money laundering paths on Ethereum involving DeFi, bridges, and mixers for improved regulatory compliance.
- •For financial fraud investigators: Identify previously unknown illicit bridge usage and post-mixer activities in Ethereum laundering cases for thorough investigations.
Authors
Huan Xing, Gaohe Jin, Bang Fu, Yue Li, Baoning Niu, Xing Fan
Abstract
Illicit actors exploit Ethereum's deep liquidity and diverse on-chain services to launder criminal proceeds by combining decentralized finance (DeFi) protocols, cross-chain bridges, and mixers in multi-stage processes. Existing anti-money laundering tracing methods fail to disentangle the underlying asset-flow paths in complex transactions, potentially misdirecting subsequent tracing; cannot identify cross-chain intent without pre-collected bridge information; and treat mixers as generic exit services, overlooking post-mixer laundering flows. We present FPTracer, a framework for tracing illicit assets across Ethereum services. First, we propose a method based on the Context-Aware Transfer Tree (CATT) to recover intra-transaction asset-flow paths, which guide accurate tracing and service analysis. Second, we extract discriminative features from asset-flow and execution-evidence perspectives and train a classifier to identify transactions with cross-chain intent. Third, we leverage evidence from the upstream laundering network to analyze potential post-mixer laundering activities. Evaluation on 20 real-world laundering cases demonstrates FPTracer's effectiveness. It achieves the highest recall for both known laundering addresses (98.6%) and destinations (94.2%). It identifies all known uses of bridges in these cases and uncovers 30 previously unreported ones. Among 18 cases with identified Tornado Cash usage, FPTracer can identify potential withdrawal-side activities, which are corroborated by public reports where available.