发现新型慢速资金盗窃骗局，可长期潜伏并造成超千万美元损失
Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams
- 通过规则+机器学习方法，识别隐蔽的渐进式资金抽逃
- 检测速度比传统方法快4.77倍，准确率达95%
- 适合DeFi安全团队与投资者防范长期风险
我们首次揭示了慢速流动性耗尽（SLID）骗局，这是一种隐蔽且高收益的去中心化金融（DeFi）威胁，对生态构成大规模、持续且不断增长的风险。与传统的跑路或陷阱类骗局不同，SLID通过长时间逐步抽取流动性池资金，极难被发现。本研究对2018年以来六大主要去中心化交易所的319,166个流动性池进行了大规模实证分析，共识别出3,117个受SLID影响的池子，累计损失超过1030万美元。我们提出一种基于规则的启发式方法和一个增强型机器学习模型用于早期检测，该模型检测速度比启发式方法快4.77倍，同时保持95%的准确率。研究为早期保护投资者和提升DeFi透明度奠定了基础。
原文摘要 · Abstract (English)
We identify the slow liquidity drain (SLID) scam, an insidious and highly profitable threat to decentralized finance (DeFi), posing a large-scale, persistent, and growing risk to the ecosystem. Unlike traditional scams such as rug pulls or honeypots (USENIX Sec'19, USENIX Sec'23), SLID gradually siphons funds from liquidity pools over extended periods, making detection significantly more challenging. In this paper, we conducted the first large-scale empirical analysis of 319,166 liquidity pools across six major decentralized exchanges (DEXs) since 2018. We identified 3,117 SLID affected liquidity pools, resulting in cumulative losses of more than US$103 million. We propose a rule-based heuristic and an enhanced machine learning model for early detection. Our machine learning model achieves a detection speed 4.77 times faster than the heuristic while maintaining 95% accuracy. Our study establishes a foundation for protecting DeFi investors at an early stage and promoting transparency in the DeFi ecosystem.
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