一句话：提出 DeFort 框架，借助价格操纵行为模型、价格监控策略与利润计算机制，自动检测并分析 DeFi 价格操纵攻击。
去中心化金融（DeFi）应用允许匿名用户间不可篡改的交易，但攻击者可直接访问智能合约字节码，利用交易机制、合约代码或第三方组件中的漏洞操纵代币价格以牟利。由于价格操纵往往依赖特定状态与复杂交易序列，现有检测工具难以应对；同时审计者更关心攻击「如何」发生，而非仅确认其存在。本文提出自动价格操纵检测与分析框架 DeFort，包含指导链上检测的价格操纵行为模型、用于发现代币价格异常池的多种价格监控策略，以及确认攻击的多种利润计算机制。基于行为模型，DeFort 能自动定位引发价格异常波动的交易与函数，并识别攻击者与受害者。实验表明其性能优于现有最先进的价格操纵检测方法；在对 441 个真实项目进行两个月监控后，DeFort 成功检测到五起价格操纵攻击。
Although Decentralized Finance (DeFi) applications facilitate tamper-proof transactions among multiple anonymous users, since attackers can access the smart contract bytecode directly, vulnerabilities in the transaction mechanism, contract code, or third-party components can be easily exploited to manipulate token prices, leading to financial losses. Since price manipulation often relies on specific states and complex trading sequences, existing detection tools have limitations in addressing this problem. In addition, to swiftly identify the root cause of an attack and implement targeted defense and remediation measures, auditors typically prioritize understanding the methodology behind the attack, emphasizing 'how' it occurred rather than simply confirming its existence. To address these problems, this paper presents a novel automatic price manipulation detection and analysis framework, named DeFort, which contains a price manipulation behavior model to guide on-chain detection, multiple price monitoring strategies to detect pools with abnormal token prices, and various profit calculation mechanisms to confirm attacks. Based on behavioral models, DeFort can automatically locate transactions and functions that cause abnormal price fluctuations and identify attackers and victims. Experimental results demonstrate that DeFort can outperform state-of-the-art price manipulation detection methods. Furthermore, after monitoring 441 real-world projects for two months, DeFort successfully detected five price manipulation attacks.
@inproceedings{paperbot8610,
title = {DeFort: Automatic Detection and Analysis of Price Manipulation Attacks in DeFi Applications},
author = {Maoyi Xie and Ming Hu and Ziqiao Kong and Cen Zhang and Yebo Feng and Haijun Wang and Yue Xue and Hao Zhang and Ye Liu and Yang Liu},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3652137}
}