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ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense
arXiv – CS AI|Nancy Lau, Louis Sloot, Jyoutir Raj, Giuseppe Marco Boscardin, Evan Harris, Dylan Bowman, Mario Brajkovski, Jaideep Chawla, Dan Zhao||3 views
🤖AI Summary
Researchers introduced ZeroDayBench, a new benchmark testing LLM agents' ability to find and patch 22 critical vulnerabilities in open-source code. Testing on frontier models GPT-5.2, Claude Sonnet 4.5, and Grok 4.1 revealed that current LLMs cannot yet autonomously solve cybersecurity tasks, highlighting limitations in AI-powered code security.
Key Takeaways
- →ZeroDayBench benchmark tests LLM agents on finding and patching 22 novel critical vulnerabilities in real codebases.
- →Frontier models GPT-5.2, Claude Sonnet 4.5, and Grok 4.1 failed to autonomously solve cybersecurity tasks.
- →Current LLMs lack the capability for effective proactive cyberdefense despite being deployed as software engineering agents.
- →The research identifies behavioral patterns that could guide improvements in AI cybersecurity capabilities.
- →Results suggest significant gaps remain between AI agent deployment and their actual security analysis competence.
#llm-agents#cybersecurity#zero-day#vulnerability-detection#ai-benchmarks#code-security#frontier-models#software-engineering#ai-limitations
Read Original →via arXiv – CS AI
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