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P2 Vision: Identifying Affected Library Versions for Open Source Software Vulnerabilities

Vision:识别开源软件漏洞的受影响库版本
ASE Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering2024匹配度 2/10 · 该论文属于软件安全与程序分析,主题是开源软件漏洞受影响版本识别,与用户画像核心的 LLM/Agent×静态分析、浮点程序分析、贝叶斯程序分析均无直接交叉,也未涉及 Agentic 程序或 LLM 改进分析。引用 2 inbox
Susheng Wu, Ruisi Wang, Kaifeng Huang, Yiheng Cao, Wenyan Song, Zhuotong Zhou, Yiheng Huang, Bihuan Chen, Xin Peng
crossref: 2026-09-12

AI 速览

一句话:提出识别开源软件漏洞受影响库版本的新思路,强调跨包注册表一致性并区分漏洞方法与补丁语句的重要性。

本文是一篇愿景论文,聚焦开源软件漏洞报告中受影响库版本不准确的问题。由于多版本及其差异,维护版本准确性尤其困难。现有识别方法存在两个局限:部分方法依赖 GitHub 等代码托管平台,所得版本与 Maven 等包注册表中的版本并不总是一致;此外,当漏洞涉及多个方法和多个变更块时,现有方法未能区分不同漏洞方法和补丁语句的重要性。本文据此提出识别受影响库版本的研究愿景,旨在克服上述局限,提升漏洞报告中受影响版本识别的准确性和一致性。

问题
开源软件漏洞报告中的受影响版本常不准确,尤其在多版本及其差异下;现有方法还存在与包注册表版本不一致、无法区分漏洞方法与补丁语句重要性等局限。
方法
作为 Vision 论文,本文主要剖析现有方法的局限并提出更准确识别受影响库版本的研究方向;摘要未展开具体技术细节。
评估
摘要未报告实验评估;论文属于愿景性工作,以问题分析和方向论证为主。
原文摘要(Abstract)

Vulnerability reports play a crucial role in mitigating open-source software risks. Typically, the vulnerability report contains affected versions of a software. However, despite the validation by security expert who discovers and vendors who review, the affected versions are not always accurate. Especially, the complexity of maintaining its accuracy increases significantly when dealing with multiple versions and their differences. Several advances have been made to identify affected versions. However, they still face limitations. First, some existing approaches identify affected versions based on repository-hosting platforms (i.e., GitHub), but these versions are not always consistent with those in package registries (i.e., Maven). Second, existing approaches fail to distinguish the importance of different vulnerable methods and patched statements in face of vulnerabilities with multiple methods and change hunks.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot8359,
  title = {Vision: Identifying Affected Library Versions for Open Source Software Vulnerabilities},
  author = {Susheng Wu and Ruisi Wang and Kaifeng Huang and Yiheng Cao and Wenyan Song and Zhuotong Zhou and Yiheng Huang and Bihuan Chen and Xin Peng},
  booktitle = {ASE},
  year = {2024},
  doi = {10.1145/3691620.3695516}
}