论文标题

SOK:分散财务(DEFI)攻击

SoK: Decentralized Finance (DeFi) Attacks

论文作者

Zhou, Liyi, Xiong, Xihan, Ernstberger, Jens, Chaliasos, Stefanos, Wang, Zhipeng, Wang, Ye, Qin, Kaihua, Wattenhofer, Roger, Song, Dawn, Gervais, Arthur

论文摘要

在短短四年内,基于区块链的分散融资(DEFI)生态系统积累了超过2530亿美元的峰值总价值(TVL)。不幸的是,Defi受欢迎程度的这种激增伴随着许多有影响力的事件。根据我们的数据,用户,流动性提供者,投机者和协议运营商的总损失至少为32.4亿美元,从2018年4月30日到2022年4月30日。鉴于区块链的透明度和增加的事件频率,出现了两个问题:我们如何系统地衡量,评估,评估,评估和比较异常事件?我们如何从过去的攻击中学习以增强Defi安全性? 在本文中,我们引入了一个共同的参考框架,以系统地评估和比较违规事件,包括攻击和事故。我们调查了77篇学术论文,30份审计报告和181起现实事件。我们的数据揭示了学术界与从业者社区之间的几个差距。例如,很少有学术论文介绍“价格甲骨文攻击”和“无宽松的互动”,而我们的数据表明它们是两种最常见的事件类型(相应地,15%和10.5%)。我们还调查了潜在的防御措施,并发现:(i)103(56%)的攻击未在原子上执行,为防御者提供了救援时间; (ii)SOTA字节码相似性分析至少可以检测31个脆弱/23个对抗合同; (iii)33(15.3%)的对手通过与集中式交易所相互作用,潜在地识别了可识别的信息。

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi's popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain's transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners' community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges.

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