# ARVO: Atlas of Reproducible Vulnerabilities for Open Source Software

Source: https://arxiv.org/abs/2408.02153
Kiwi News: https://news.kiwistand.com/stories/ARVO-Atlas-of-Reproducible-Vulnerabilities-for-Open-Source-Software?index=0x66b79365d32358a9523cfc769ec3c45f7e3817e7fc76a83008c3c945eb2d26a0e731b57b
Submitted by misha on 2024-08-10 UTC, 1 upvote, 0 comments

## Summary

ARVO is a dataset of over 6,100 reproducible real-world vulnerabilities across 311 open-source projects, built by making the OSS-Fuzz vulnerability collection fully reproducible. The authors, including Xiang Mei and Brendan Dolan-Gavitt, argue that reproducibility has long been the most neglected dimension of vulnerability datasets, which limits automatic extraction and downstream security research. Each vulnerability can be consistently rebuilt, triggered, and analyzed across versions, which enables automatic identification of the corresponding patch. In the authors' evaluation, ARVO reproduces 81% of vulnerabilities and achieves 89.4% accuracy on the located patches.

## Article

ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software

Authors: Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doupé, Hammond Pearce, Brendan Dolan-Gavitt

Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others. In practice, reproducibility has been the dimension most often neglected. This has limited what can be automatically extracted from historical bug datasets, and has reduced their utility for downstream security research.

In this work, we propose a method to produce a new security dataset which ensures reproducibility for diverse vulnerabilities at scale by identifying the key obstacles to large-scale bug reproduction and addressing them with general solutions. Using this method, we introduce full reproducibility to the largest open source software vulnerability dataset (OSS-Fuzz) and construct the ARVO dataset (an Atlas of Reproducible Vulnerabilities in Open-source software). ARVO is a large-scale dataset consisting of over 6,100 real-world vulnerabilities across 311 projects. Focusing on reproducibility, ARVO differs from existing datasets by providing each vulnerability in a form that can be consistently rebuilt, triggered, and analyzed across versions. Reproducibility also enables automatic identification of the corresponding patch for each vulnerability and supports direct interaction with vulnerabilities after code changes, capabilities that existing large-scale datasets do not provide. In our evaluation, ARVO successfully reproduces 81% of vulnerabilities and achieves 89.4% accuracy on the located patches. We also discuss ARVO's influence on both upstream practices and downstream security research.