Trident: a single 7B planner degrades deep-RL cyber defenses by 522% versus static red-team baselines

arXiv·medium signalXBlueskyLinkedInCopy link

arXiv 2608.04317 (Aug 5) points out that autonomous cyber-defense agents are almost always evaluated against static heuristic attackers. Trident supplies a dynamic benchmark of isolated sandbox servers spanning CybORG CAGE 4 and CyberWheel, a dataset of 13,000+ red-blue interaction trajectories for RLVR training, and a Code-as-Policy architecture that splits the red agent into Log Summarizer, trainable Planner and frozen Coder — the Planner writes attack strategy from compressed logs, the Coder compiles it into executable Python deployed against live defenders. With one trainable 7B planner it cut blue-agent performance by an average of 522% and autonomously discovered decoy avoidance and adaptive state prioritization that heuristic red agents never surface.