In this paper, we explore the potential of Large Language Models (LLMs) to reason about threats, generate information about tools, and automate cyber campaigns. We begin with a manual exploration of LLMs in supporting specific threat-related actions and decisions. We proceed by automating the decision process in a cyber campaign. We present prompt engineering approaches for a plan-act-report loop for one action of a threat campaign and and a prompt chaining design that directs the sequential decision process of a multi-action campaign. We assess the extent of LLM's cyber-specific knowledge w.r.t the short campaign we demonstrate and provide insights into prompt design for eliciting actionable responses. We discuss the potential impact of LLMs on the threat landscape and the ethical considerations of using LLMs for accelerating threat actor capabilities. We report a promising, yet concerning, application of generative AI to cyber threats. However, the LLM's capabilities to deal with more complex networks, sophisticated vulnerabilities, and the sensitivity of prompts are open questions. This research should spur deliberations over the inevitable advancements in LLM-supported cyber adversarial landscape.

本研究探索了大型语言模型在威胁推理、工具信息生成和自动化网络攻击方面的潜力，讨论了LLM在支持特定威胁相关行动和决策方面的手动与自动化探索，以及对威胁网络潜在影响和使用LLM加速威胁行为能力的伦理考量，对诱导可操作反应的提示设计进行了评估和启发，并提出了探索LLM在更复杂网络、高级漏洞和提示敏感性方面的未解问题。

LLMs杀死了脚本小子：大语言模型支持的代理如何改变网络威胁测试的现状