This episode covers SecurityBot, an advanced Large Language Model (LLM) agent designed to improve cybersecurity operations by combining the strengths of LLMs and Reinforcement Learning (RL) agents. SecurityBot uses a collaborative architecture where LLMs leverage their contextual knowledge, while RL agents, acting as mentors, provide local environment expertise. This hybrid approach enhances performance in both attack (red team) and defense (blue team) cybersecurity tasks.
Key components of SecurityBot's architecture include:
- LLM Agent with modules for profiling, memory, action, and reflection.
- RL Agent Pool of pre-trained RL mentors (A3C, DQN, PPO) to assist the LLM agent.
- Collaboration mechanisms like the Cursor, Aggregator, and Caller that facilitate the interaction between the LLM and RL agents.The episode also details SecurityBot's performance in simulated tasks:
- In red team tasks, SecurityBot excels when collaborating with a strong RL mentor, while multiple mentors can create noise.
- In blue team tasks, LLM agents outperform RL agents, with minimal benefit from RL mentors.The episode concludes with discussions on future improvements, such as enhancing mentor selection strategies and fine-tuning LLMs for cybersecurity.
https://arxiv.org/pdf/2403.17674v1