Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams
It addresses a persistent and growing threat to DeFi investors by enabling early detection of a hard-to-spot scam.
The paper identifies the slow liquidity drain (SLID) scam in decentralized finance (DeFi), which has caused over $103 million in losses across 3,117 affected pools, and proposes a machine learning model that detects it 4.77 times faster than a heuristic with 95% accuracy.
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.