High-quality datasets of real-world vulnerabilities are enormously valuable for downstream research in software security, but existing datasets are typically small, require extensive manual effort to update, and are missing crucial features that such research needs. In this paper, we introduce ARVO: an Atlas of Reproducible Vulnerabilities in Open-source software. By sourcing vulnerabilities from C/C++ projects that Google's OSS-Fuzz discovered and implementing a reliable re-compilation system, we successfully reproduce more than 5,000 memory vulnerabilities across over 250 projects, each with a triggering input, the canonical developer-written patch for fixing the vulnerability, and the ability to automatically rebuild the project from source and run it at its vulnerable and patched revisions. Moreover, our dataset can be automatically updated as OSS-Fuzz finds new vulnerabilities, allowing it to grow over time. We provide a thorough characterization of the ARVO dataset, show that it can locate fixes more accurately than Google's own OSV reproduction effort, and demonstrate its value for future research through two case studies: firstly evaluating real-world LLM-based vulnerability repair, and secondly identifying over 300 falsely patched (still-active) zero-day vulnerabilities from projects improperly labeled by OSS-Fuzz.

本研究解决了现有软件安全漏洞数据集小、更新繁琐以及缺乏关键特征的问题。提出了ARVO，一个自动化更新的可复现漏洞数据集，通过对Google OSS-Fuzz发现的C/C++项目的漏洞进行复现，提供了超过5000个内存漏洞的详细信息。研究表明，ARVO在精确定位修复方面优于Google的OSV复现工作，并在未来研究中展现了其价值。

可复现漏洞的开放源代码软件地图（ARVO）