On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study
Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security analysts typically assign CVSS scores, but this manual triage does not scale with the growth of disclosed vulnerabilities and often depends on cloud LLM services that raise confidentiality concerns. This paper presents an industrial case study on predicting CVSS v3.1 scores directly from vulnerable C/C++ snippets using in-context learning with locally deployable, open-source LLMs. We compare proprietary data with the Big-Vul dataset, showing sufficiently aligned CVSS distributions to justify Big-Vul as a proxy for industrial data when const...
| Authors | Daniel Rodriguez-Cardenas, David Nader Palacio, Anna Schmedding et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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