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Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams

Abstract

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 US103million.Weproposearulebasedheuristicandanenhancedmachinelearningmodelforearlydetection.Ourmachinelearningmodelachievesadetectionspeed4.77timesfasterthantheheuristicwhilemaintaining95103 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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@article{tran2025_2503.04850,
  title={ Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams },
  author={ Minh Trung Tran and Nasrin Sohrabi and Zahir Tari and Qin Wang and Xiaoyu Xia },
  journal={arXiv preprint arXiv:2503.04850},
  year={ 2025 }
}
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