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AI Research
arXiv CS.AI·May 25·8 min read
Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks
TL;DR
Large language models (LLMs) are being tested for cybersecurity tasks, but their readiness is unclear. Researchers created dual-mode benchmarks: white-box code vulnerability detection and black-box web application security testing across six frontier models and two specialized models.
✦ Why It Matters
Engineers should prioritize domain-specialized security models and structured methodology over scaling general LLMs for vulnerability detection.
Key Takeaways
- Frontier LLMs show 10-50% false positive rates in vulnerability detection.
- Only 4-8% ground-truth coverage achieved in black-box testing.
- Domain-specialized models outperform frontier LLMs significantly.
- Structured security testing data is crucial for improving model performance.
How It Works
The study uses a dual-mode benchmark to evaluate LLMs: white-box testing analyzes code for vulnerabilities, while black-box testing assesses web applications for security flaws. The models are tested against known vulnerabilities, allowing for a direct comparison of their detection capabilities.