Trident: Agentic LLM Framework to Stress-Test DRL Cyber Defenses

A recent paper published on arXiv (2608.04317) presents Trident, a framework for red teaming agentic LLMs aimed at assessing and attacking autonomous cyber defense systems that utilize Deep Reinforcement Learning (DRL). This cross-type submission highlights that existing DRL cyber defenses have primarily been evaluated against static heuristic red agents, leaving their resilience to adaptive threats largely unexplored. Trident includes three main elements: a dynamic benchmark featuring isolated sandbox servers from CybORG CAGE 4 and CyberWheel; a dataset containing over 13,000 detailed red-blue interaction trajectories for Reinforcement Learning with Verifiable Rewards (RLVR); and a 'Code-as-Policy' RLVR architecture (Trident Agentic) that redefines red agent training as a contextual bandit. This framework seeks to connect advancements in RLVR for LLM reasoning with cybersecurity applications, addressing the lack of appropriate benchmark environments and interaction datasets. Authored by a team of researchers, the paper is accessible via the provided arXiv link and is poised to enhance the cybersecurity field by offering a more adaptive threat model for DRL-based defense evaluation, potentially resulting in stronger autonomous cyber defense systems.