Ruitao Feng, Yuekang Li, Yao Zhang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.24623 · open-access PDF
As plataformas de recompensa por bugs recebem relatórios de vulnerabilidade mais rapidamente do que os fornecedores podem classificá-los, e os Grandes Modelos de Linguagem (LLMs) são cada vez mais considerados para atribuir a gravidade do Sistema Comum de Classificação de Vulnerabilidade (CVSS) que impulsiona a priorização. As avaliações anteriores classificam as descrições curtas do CVE e a precisão do relatório, mas a implantação depende de como os modelos erram, não apenas de quantas vezes.
Machine translation (Meta M2M100); the English original follows.
Bug bounty platforms receive vulnerability reports faster than vendors can triage them, and Large Language Models (LLMs) are increasingly considered for assigning the Common Vulnerability Scoring System (CVSS) severity that drives prioritization. Prior evaluations score short CVE descriptions and report accuracy, but deployment depends on how models err, not only how often. CVSS gives each metric a formal meaning that departs from how researchers describe impact in narrative reports, and model rationales suggest that errors concentrate where the two diverge; we ask whether this pattern is syst
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