贷款
计算机科学
闪光灯(摄影)
计算机安全
数字加密货币
利润(经济学)
资产(计算机安全)
财务
业务
经济
微观经济学
艺术
视觉艺术
作者
Qing Xia,Zhirong Huang,Wensheng Dou,Yafeng Zhang,Fengjun Zhang,Geng Liang,Chun S. Zuo
标识
DOI:10.1109/icdcs57875.2023.00078
摘要
Decentralized Finance (DeFi) ecosystem has grown rapidly in the past few years. In the DeFi ecosystem, flash loan is a novel type of uncollateralized loan with nearly negligible lending costs. Malicious attackers can easily borrow a large number of crypto assets, and utilize them to disrupt the price of crypto assets to make a profit. Many flash loan based price manipulation attacks have been reported recently, and caused immense economic losses, e.g., 30 million USD in a single attack. In this paper, we conduct an empirical study on real-world flash loan based attacks in the past two years and present three attack patterns for price manipulation attacks. Then, we propose an approach, LeiShen, to automatically detect price manipulation attacks with asset transfers. We evaluate LeiShen on the first 14,500,000 blocks in Ethereum, and detect 180 attacks with a precision of 78.9%. Among our newly-found attacks, the severest attack has caused a total loss of more than 6.1 million USD.
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