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This work presents an end-to-end diagnostic study of an Autonomous Adversary system with orchestrator, executor, and validator LLMs in enterprise-like lateral-movement scenarios and uses comparative LLM-as-a-Judge analysis to identify planning deficiencies.
This work surveys recent LLM-based systems across seven core domains and identifies the need for privacy-aware deployment, timely retrieval and knowledge maintenance for emerging threats, process-level evaluation tied to measurable security outcomes, and human oversight within controlled and hybrid automation workflows...
As cyber threats continue to evolve, there is a need for Autonomous Cyber Defense (ACD) strategies capable of fast and context-aware responses. Reinforcement learning (RL) has shown promise in automating cyber defense by exploring and learning effective countermeasures. However, RL often struggles with sparse reward si...
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