Proving the Utility of Large Language Models in Cybersecurity Simulations: A Comprehensive Examination

Abstract

Cyber threats continue to escalate in both frequency and sophistication, necessitating more adaptive and scalable defense strategies. This paper explores how Large Language Models (LLMs) can bolster cybersecurity simulations by automating the creation of synthetic environments and identifying latent vulnerabilities. We employ YAML as a structured representation format for simulating complex network configurations, thereby enabling Large Language Model-driven pipelines to support and improve reinforcement learning (RL) agent training. Comparative studies examine the advantages of LLM-based techniques over classical approaches such as Double Q-learning with Prioritized Experience Replay (PER), emphasizing increased efficiency, higher adaptability, and enhanced realism in cyberattack simulations. In empirical benchmarks across multiple synthetic topologies, LLM-instantiated Python agents achieved up to a 94.5% compromise rate while executing in 0.02-0.06 seconds per assessment---a ~25,000x to 50,000x speedup over traditional RL training cycles. Our findings underscore the transformative potential of integrating LLMs into cybersecurity research, ultimately paving the way for more intelligent and robust cyber-defense systems.