Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks
A recent study evaluates the readiness of frontier large language models (LLMs) for cybersecurity tasks. The findings indicate that these models exhibit significant limitations, including high false positive rates and low ground-truth coverage in vulnerability detection. The research suggests that specialized models and structured methodologies are more effective for cybersecurity applications.
- ▪Every frontier model produces 10-50% false positive rates in white-box detection.
- ▪In black-box testing, frontier models achieve only 4-8% ground-truth coverage.
- ▪A domain-specialized defense model achieves the highest precision of 0.904 and the lowest false positive rate of 9.7%.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23243 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
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Computer Science > Cryptography and Security arXiv:2605.23243 (cs) [Submitted on 22 May 2026] Title:Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks Authors:Vivek Dahiya, Sunny Nehra, Vipul Dholariya, Bhavik Shangari, Chandra Khatri View a PDF of the paper titled Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks, by Vivek Dahiya and 4 other authors View PDF HTML (experimental) Abstract:We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and black-box web application security testing (five production-style applications with…
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