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Open-source LLMs show promise for predicting software vulnerability severity

By PulseAugur Editorial · [1 sources] ·

Researchers have conducted an industrial case study on predicting CVSS v3.1 scores for software vulnerabilities using in-context learning with locally deployable, open-source Large Language Models (LLMs). The study compared proprietary data with the Big-Vul dataset, finding them sufficiently aligned to use Big-Vul as a proxy for industrial data. Results indicated that medium-sized open-source code models, specifically CodeLlama2-7B, can achieve performance comparable to cloud-based models for CVSS regression when guided by lightweight, output-constraining prompts, offering a privacy-preserving solution for industrial vulnerability triage. AI

IMPACT Offers a privacy-preserving, locally deployable alternative for industrial vulnerability severity assessment.

RANK_REASON Academic paper presenting a case study on LLM application for vulnerability severity prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open-source LLMs show promise for predicting software vulnerability severity

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Academic paper presenting a case study on LLM application for vulnerability severity prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Daniel Rodriguez-Cardenas, David Nader Palacio, Anna Schmedding, Yiyang Lu, Aadil Mallick, Bill Hudson, Chris Gourley, Michael Roytman, Chris Shenefiel, Evgenia Smirni, Denys Poshyvanyk ·

    On Predicting Vulnerability Severity Using In-Context Learning: An Industrial Case Study

    arXiv:2608.22089v1 Announce Type: cross Abstract: 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 wi…

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