Large Language Models (LLMs) have gained widespread popularity across diverse
domains involving text generation, summarization, and various natural language
processing tasks. Despite their inherent limitations, LLM-based designs have
shown promising capabilities in planning and navigating open-world scenarios.
This paper introduces a novel application of pre-trained LLMs as agents within
cybersecurity network environments, focusing on their utility for sequential
decision-making processes.
We present an approach wherein pre-trained LLMs are leveraged as attacking
agents in two reinforcement learning environments. Our proposed agents
demonstrate similar or better performance against state-of-the-art agents
trained for thousands of episodes in most scenarios and configurations. In
addition, the best LLM agents perform similarly to human testers of the
environment without any additional training process. This design highlights the
potential of LLMs to efficiently address complex decision-making tasks within
cybersecurity.
Furthermore, we introduce a new network security environment named
NetSecGame. The environment is designed to eventually support complex
multi-agent scenarios within the network security domain. The proposed
environment mimics real network attacks and is designed to be highly modular
and adaptable for various scenarios.

利用预训练的大型语言模型作为网络安全环境中攻击代理，模拟真实网络攻击并在复杂决策任务中展现良好性能。