AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges
Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation. However, evaluating their offensive capabilities remains constrained by limited access to open, reproducible, multi-host cyber ranges. Existing public benchmarks capture isolated skills such as CTF solving, vulnerability reproduction, and exploit generation, but often abstract away realistic intrusion workflows: discovering exposed services, gaining a foothold, collecting internal information, and expanding compromise across hosts. This gap makes it difficult to observe emerging risks early, because frontier AI systems are rarely evaluated under re...
| Authors | Fengyu Liu, Jiarun Dai, Yihe Fan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 12 June 2026 |
| Last updated at source | 16 June 2026 |
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