LLMs Distilled into Lightweight Agents for Cyber Defense

Researchers have explored a novel approach to enhance autonomous cyber operations (ACO) by leveraging large language models (LLMs) and knowledge distillation. The study shows that an 8-billion parameter LLM, specifically pretrained on cybersecurity data, can outperform a standard reinforcement learning (RL) agent in a simulated cyber defense environment, even without fine-tuning, simply through effective prompt engineering. A key innovation is the development of an online policy distillation framework. This framework successfully transfers the LLM's sophisticated defensive policy into a significantly smaller, lightweight RL agent, reducing the model size by several orders of magnitude to just 64,910 parameters. This process maintains effective defensive capabilities, offering a practical pathway to deploy advanced cybersecurity models in resource-constrained environments. The research also evaluated transferability across varying network configurations and found that direct policy alignment between reward-driven RL and teacher-guided strategies has limitations.