Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)

Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, heuristic red agents, leaving their robustness against adaptive threats critically understudied. Meanwhile, recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have improved LLM reasoning, but their integration into cybersecurity remains elusive due to the absence of suitable benchmark environments

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ethics.ai (5 August 2026), “Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic),” evidence record 16969, https://ethics.ai/record/16969 (originally published by arXiv red teaming query).

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