一句话:提出 EarlyVulnFix,利用数据流与依赖分析在漏洞公开披露前识别开源项目中的静默污点型漏洞修复。
开源软件广泛采用协同漏洞披露模型,但安全修复往往在正式披露前已进入公开仓库,攻击者可提前发现并利用漏洞。本文针对污点型漏洞(攻击者可控输入未净化即到达敏感操作)的静默修复识别问题,提出 EarlyVulnFix。该方法基于数据流与依赖分析,针对两种常见修复场景区分新引入代码与 sink 之间的两类连接,从而检测未公开的漏洞修复。评估表明,EarlyVulnFix 在 F1 值上显著优于现有基线;在七个项目的 700 个最新提交上,它在其安全版本发布前检测到三个安全修复,证明了在真实场景中发现未报告漏洞修复的有效性。
The coordinated vulnerability disclosure model, widely adopted in open-source software (OSS) organizations, recommends the silent resolution of vulnerabilities without revealing vulnerability information until their public disclosure. However, the inherently public nature of OSS development leads to security fixes becoming publicly available in repositories weeks before the official disclosure of vulnerabilities. This time gap poses a significant security risk to OSS users, as attackers could discover the fix and exploit vulnerabilities before disclosure. Thus, there is a critical need for OSS users to sense fixes as early as possible to address the vulnerability before any exploitation occurs. In response to this challenge, we introduce EarlyVulnFix, a novel approach designed to identify silent fixes for taint-style vulnerabilities—a persistent class of security weaknesses where attacker-controlled input reaches sensitive operations (sink) without proper sanitization. Leveraging data flow and dependency analysis, our tool distinguishes two types of connections between newly introduced code and sinks, tailored for two common fix scenarios. Our evaluation demonstrates that EarlyVulnFix surpasses state-of-the-art baselines by a substantial margin in terms of F1 score. Furthermore, when applied to the 700 latest commits across seven projects, EarlyVulnFix detected three security fixes before their respective security releases, highlighting its effectiveness in identifying unreported vulnerability fixes in the wild.
DOI 原文 ·
@inproceedings{paperbot8460,
title = {Silent Taint-Style Vulnerability Fixes Identification},
author = {Zhongzhen Wen and Jiayuan Zhou and Minxue Pan and Shaohua Wang and Xing Hu and Tongtong Xu and Tian Zhang and Xuandong Li},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3652139}
}