面向高价值智能合约漏洞的程序分析：技术与见解
摘要
区块链安全社区广泛认为，自动化技术仅适用于检测浅层漏洞，通常价值较小。本文呈现我们实现自动发现高价值智能合约漏洞的技术与见解。我们的漏洞披露已获得10个bug奖励，总额超过300万美元，针对的是高度知名的部署代码，以及在部署前或审计期间检测到的数百个漏洞。我们认为，这种意想不到的成功因素包括：a) 一种高完备性的静态分析方法，能够在保持可接受精度的同时实现；b) 来自专家提供的领域知识或通过统计推断捕获的领域知识。我们提出了 novel techniques for automatically inferring domain knowledge from statistical analysis of a large corpus of deployed contracts, as well as discuss insights on the ideal precision and warning rate of a promising vulnerability detector. In contrast to academic literature in program analysis, which routinely expects false-positive rates below 50% for publishable results, we posit that a useful analysis for high-value real-world vulnerabilities will likely flag very few programs (under 1%) and will do so with a high false-positive rate (e.g., 95%, meaning that only one-of-twenty human inspections will yield an exploitable vulnerability).
引用
@article{arxiv.2507.20672,
title = {Program Analysis for High-Value Smart Contract Vulnerabilities: Techniques and Insights},
author = {Yannis Smaragdakis and Neville Grech and Sifis Lagouvardos and Konstantinos Triantafyllou and Ilias Tsatiris and Yannis Bollanos and Tony Rocco Valentine},
journal= {arXiv preprint arXiv:2507.20672},
year = {2025}
}