Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games
<|reference_start|>Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games: Integrating LLM and reinforcement learning (RL) agent effectively to achieve complementary performance is critical in high stake tasks like cybersecurity operations. In this study, we introduce SecurityBot, a LLM agent mentored by pre-trained RL agents, to support cybersecurity operations. In particularly, the LLM agent is supported with a profile module to generated behavior guidelines, a memory module to accumulate local experiences, a reflection module to re-evaluate choices, and an action module to reduce action space. Additionally, it adopts the collaboration mechanism to take suggestions from pre-trained RL agents, including a cursor for dynamic suggestion taken, an aggregator for multiple mentors' suggestions ranking and a caller for proactive suggestion asking. Building on the CybORG experiment framework, our experiences show that SecurityBot demonstrates significant performance improvement compared with LLM or RL standalone, achieving the complementary performance in the cybersecurity games.<|reference_end|>
@article{yan2024depending,
    title={Depending on yourself when you should: Mentoring LLM with RL agents to
  become the master in cybersecurity games},
    author={Yikuan Yan, Yaolun Zhang, Keman Huang},
    journal={arXiv preprint arXiv:2403.17674},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17674},
    primaryClass={cs.CR cs.AI cs.MA}
}