arXiv:2606.17283v1 Announce Type: cross Abstract: 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
The increasing reliance on open-source software for critical AI and other systems necessitates more robust and reproducible vulnerability research, a gap this work aims to address.
Improved, reproducible vulnerability datasets will significantly enhance security research, leading to more secure open-source software critical for various technological infrastructures.
The ability to reliably reproduce and study software vulnerabilities means security analysis can become more rigorous and automated, directly impacting the robustness of modern software stacks.
- · Cybersecurity researchers
- · Open-source software foundations
- · AI development platforms
- · Organizations relying on open-source software
- · Malicious actors exploiting unknown vulnerabilities
- · Software maintainers with weak security practices
Security tooling and AI models for vulnerability detection will become more effective and accurate due to better training data.
A reduction in critical security incidents stemming from known but hard-to-reproduce open-source vulnerabilities could be observed.
This could lead to new regulatory pressures for software suppliers to demonstrate reproducible security testing for open-source components.
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Read at arXiv cs.AI