Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909723816624128 |
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| author | Tran, Minh Trung Sohrabi, Nasrin Tari, Zahir Wang, Qin Xue, Minhui Xia, Xiaoyu |
| author_facet | Tran, Minh Trung Sohrabi, Nasrin Tari, Zahir Wang, Qin Xue, Minhui Xia, Xiaoyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04850 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams Tran, Minh Trung Sohrabi, Nasrin Tari, Zahir Wang, Qin Xue, Minhui Xia, Xiaoyu Cryptography and Security Machine Learning 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. |
| title | Slow is Fast! Dissecting Ethereum's Slow Liquidity Drain Scams |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2503.04850 |