CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios

ArXi:2605.07830v1 Announce Type: cross Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack-selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we