CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
To address this gap, we present CyberArena, a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat.
ProofPaper ↗
Key points
- AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world.
- Current benchmarks mostly evaluate agents in isolation against static targets.
- Furthermore, because pairwise contests are notoriously hard to quantify, we introduce a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities.
- Evaluating 12 frontier LLM agents across 40 challenges in 466 matches, CyberArena separates model capabilities where single-agent baselines hit a ceiling.
Sources (1)
- [1]CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense CombatarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:05 PM
To address this gap, we present CyberArena, a dynamic, symmetric adversarial benchmark built around multi-round attack-and-defense combat.
AI agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world.
Extractive summary: sentences quoted from the sources.
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