arXiv:2602.08023v3 Announce Type: replace-cross Abstract: Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objective. They measure exploitation effectively, but miss how real Capture-the-Flag (CTF) participants triage unknown surfaces, prioritize targets, and allocate effort under uncertainty. Current evaluations therefore fail to assess strategic reasoning beyond exploitation alone. To address this, we introduce \textit{CTFExplorer}, a benchmark suite that shifts offensive security evaluation toward a mult
The rapid advancement and deployment of LLMs necessitate more robust and realistic evaluation methods for their capabilities, especially in complex, adversarial fields like cybersecurity.
This benchmark addresses a critical gap in assessing the strategic reasoning and decision-making of AI agents beyond simple exploitation, moving towards more human-like offensive capabilities.
The shift from single-target, known vulnerability benchmarks to multi-target, unknown surface CTF scenarios enables a more comprehensive evaluation of LLM offensive agents' strategic intelligence.
- · AI offensive security researchers
- · Organizations developing defensive AI systems
- · Cybersecurity training platforms
- · Developers relying on outdated LLM security benchmarks
- · Organizations with weak cyber defenses
Improved evaluation leads to more sophisticated and capable AI offensive agents.
This drives a faster arms race between AI-powered offensive and defensive cybersecurity systems.
The complexity of cyber warfare escalates dramatically, demanding entirely new paradigms of human-AI collaboration in defense.
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Read at arXiv cs.AI