Shedding Light on Shadows: Automatically Tracing Illicit Money Flows on EVM-Compatible Blockchains
Huo, Yicheng Hu, Yufeng Zhou, Yajin Yu, Ting Wu, Lei Wang, Cong
Huo, Yicheng
Hu, Yufeng
Zhou, Yajin
Yu, Ting
Wu, Lei
Wang, Cong
Supervisor
Department
Computer Science
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
The pseudo-anonymity and rapidly expanding ecosystem of Decentralized Finance (DeFi) have brought about significant liquidity on EVM-compatible blockchains, making them lucrative targets for cybercriminals. In the modern financial landscape, the need for an automated, high-speed, and effective illicit money tracing system is more urgent than ever to support regulators, on-chain service providers and security practitioners in their efforts to combat the frequent and large-scale occurrences of cyber financial crimes. In this paper, we propose MFTracer, an automated system for tracing illicit money flows on EVM-compatible blockchains. Against the backdrop of a domain where tracing remains labor-intensive and expert-driven, MFTracer is developed in response to two pressing real-world demands: operational efficiency and forensic effectiveness. In response to the sophisticated fund transfer mechanisms enabled by the EVM environment, we introduce a novel fine-grained technique that enables protocol-agnostic transaction-level fund flow analysis. We further propose MFA, a lightweight and purpose-built graph abstraction with a tailored storage backend, to support efficient data retrieval. We also present a simulation algorithm for downstream illicit flow discovery. We implemented MFTracer. Its infrastructure for data retrieval achieves 3.7× to 9.4× higher storage efficiency while being 14.1× to 300× faster than the leading graph database systems. Furthermore, applied to real-world cybercrime incidents, MFTracer achieved 94.09% coverage of illicit money flows. It also newly reported 686 blockchain addresses and 4183 related transactions involved in money laundering that were previously undiscovered. MFTracer was able to reconstruct complete fund flow trajectories and provide strong evidence to investigators for $120.9 million in stolen assets.
Citation
HuoYicheng, HuYufeng, ZhouYajin, YuTing, WuLei, and WangCong, “Shedding Light on Shadows: Automatically Tracing Illicit Money Flows on EVM-Compatible Blockchains,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 9, no. 3, pp. 1–35, Dec. 2025, doi: 10.1145/3771578
Source
Proceedings of the ACM on Measurement and Analysis of Computing Systems
Conference
Keywords
Anti-Money Laundering, Blockchain, Illicit Money Flow Tracing
Subjects
Source
Publisher
Association for Computing Machinery