Eduardo Santana de Almeida

Claudio Segal, Paulo Segal, Carlos Eduardo Banjar et al.

GitHub Security Advisories (GHSA) have become a central component of open-source vulnerability disclosure and are widely used by developers and security tools. A distinctive feature of GHSA is that only a fraction of advisories are reviewed by GitHub, while the mechanisms associated with this review process remain poorly understood. In this paper, we conduct a large-scale empirical study of the GHSA review processes, analyzing over 288,000 advisories spanning 2019-2025. We characterize which advisories are more likely to be reviewed, quantify review delays, and identify two distinct review-latency regimes: a fast path dominated by GitHub Repository Advisories (GRAs) and a slow path dominated by NVD-first advisories. We further develop a queueing model that accounts for this dichotomy based on the structure of the advisory processing pipeline.

Felipe de Sant'Anna Paixão, Joanna C. S. Santos, Paulo Anselmo da Mota Silveira Neto et al.

We study how security patches in highly configurable C/C++ systems map onto the space of compile-time variants. We formalize the Vulnerability Impact Condition (VIC) - a Boolean predicate over configuration options that denotes all variants that contained the original flaw - and introduce PatchLens, a purely static technique that recovers VICs by aligning AST-level patch hunks with source-level presence conditions and resolving file inclusion via lightweight build system analysis. Evaluating PatchLens on 1,192 Linux kernel, 289 FFmpeg, and 100 PHP patches, we compute precise, human-readable VICs without the need to compile any system variant. The resulting predicates are compact (avg. 1.84 variables for Linux, 3.23 for FFmpeg, 1.04 for PHP) and show that only a small fraction of vulnerabilities are system-wide, which carry higher CVSS scores; meanwhile, CVE texts almost never encode the required options ($\approx$ 1% average recall), motivating automated enrichment of CVE descriptions with VICs. PatchLens and the accompanying dataset enable immediate applications in CI (variant-aware triage and test selection), targeted sampling and fuzzing, and feature risk scoring, offering a scalable, explainable path to vulnerability assessment in highly configurable software.