# Autonomous cyber defense

The KTH team released the MAL Simulator, which builds cyber attack and defense simulation on the Meta Attack Language so a new target domain can be modeled without changing simulator source code. Across 30 scenarios derived from the ADS-24 network in the CRATE cyber range, a trained reinforcement-learning attacker returned −301±10.3 on 15 unseen test scenarios, better than breadth-first, depth-first and random search, and a defender trained under 1% false and missed alerts returned −60355±945 against −61653±914 for a heuristic defender. Against the reinforcement-learning attacker, every defender's return dropped markedly.

The KTH team released the MAL Simulator, which builds cyber attack and defense simulation on the Meta Attack Language so a new target domain can be modeled without changing simulator source code. Across 30 scenarios derived from the ADS-24 network in the CRATE cyber range, a trained reinforcement-learning attacker returned −301±10.3 on 15 unseen test scenarios, better than breadth-first, depth-first and random search, and a defender trained under 1% false and missed alerts returned −60355±945 against −61653±914 for a heuristic defender. Against the reinforcement-learning attacker, every defender's return dropped markedly.

The work introduces Trident, an agentic LLM red-teaming framework composed of a dynamic benchmark with isolated sandbox servers spanning CybORG CAGE 4 and CyberWheel, a dataset of over 13,000 high-fidelity red-blue interaction trajectories for RLVR, and a "Code-as-Policy" RLVR agentic architecture (Trident Agentic) that reformulates red-agent training as a contextual bandit via a Log Summarizer-Planner-Coder design, where a trainable Planner generates complete attack strategies from compressed execution logs and a frozen Coder translates them into executable Python policies deployed against live DRL defenders; empirical evaluation shows that with a single trainable 7B planner, Trident reduces blue-agent defensive performance by an average of 522% compared to static red-agent baselines whil

The work introduces Trident, an agentic LLM red-teaming framework composed of a dynamic benchmark with isolated sandbox servers spanning CybORG CAGE 4 and CyberWheel, a dataset of over 13,000 high-fidelity red-blue interaction trajectories for RLVR, and a "Code-as-Policy" RLVR agentic architecture (Trident Agentic) that reformulates red-agent training as a contextual bandit via a Log Summarizer-Planner-Coder design, where a trainable Planner generates complete attack strategies from compressed execution logs and a frozen Coder translates them into executable Python policies deployed against live DRL defenders; empirical evaluation shows that with a single trainable 7B planner, Trident reduces blue-agent defensive performance by an average of 522% compared to static red-agent baselines whil