CyberArena pits LLM agents in live attack-and-defense matches
Original title: CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
Authors ran 12 frontier agents across 40 challenges in 466 matches inside isolated sandboxes.
CyberArena is a symmetric benchmark in which two agents simultaneously attack and defend continuously changing services under identical rules. An automated pipeline generates and validates new environments, and a dynamical model turns match dynamics into offensive, defensive, and domain scores. Evaluating 12 frontier LLM agents over 40 challenges and 466 matches, the authors say it separates capabilities where single-agent baselines saturate, including cases of missed patches, planted backdoors, and a custom packet sniffer.
Unconfirmed: Capability separation and the described agent behaviors are reported by the paper, not independently verified.
- Contests run in isolated sandboxes with both agents attacking and defending.
- An agentic pipeline generates, reviews, and validates new environments.
- Scoring covers offensive, defensive, and domain-specific capabilities.
- Observed behaviors include stale repair checks, later-round backdoors, and a packet sniffer.
From the original article
1 min read at huggingface.co
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. Real cyber operations, however, are dynamic and coupled: security postures constantly change as attackers and defenders adapt to each other in real time.
By Bangya Liu, Jianhong Tu, Yueyang Pan · huggingface.co
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