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New benchmark VEX-Bench tests LLM agents on software supply chain vulnerability exploitability

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced VEX-Bench, the first benchmark designed to evaluate Large Language Model (LLM) agents' capabilities in assessing the exploitability of software supply chain vulnerabilities. The benchmark comprises 75 real-world cases sourced from GitHub and validated by security experts, covering Python, Java, and Go programming languages. Initial evaluations show that while models like GPT-5.5 and Claude Opus-4.6 achieve around 80% F1 score for binary vulnerability status classification, GPT-5.5 demonstrates superior performance in fine-grained justification classification, highlighting the difficulty in moving beyond simple exploitability assessment. AI

IMPACT This benchmark could accelerate the development of more sophisticated LLM agents for cybersecurity, improving the efficiency of software supply chain vulnerability assessments.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM agents on a specific task, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New benchmark VEX-Bench tests LLM agents on software supply chain vulnerability exploitability

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The cluster describes a new academic benchmark for evaluating LLM agents on a specific task, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiahao Shi, Edward Tsien, Yifeng Di, Hongjiao Zhang, Yuan Tang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman, Ilan Pinto, Tianyi Zhang ·

    VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

    arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing defenses such as GitHub Dependabot often raise many false alerts because their coa…

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