Penetration testing, an essential component of cybersecurity, allows organizations to proactively identify and remediate vulnerabilities in their systems, thus bolstering their defense mechanisms against potential cyberattacks. One recent advancement in the realm of penetration testing is the utilization of Language Models (LLMs). We explore the intersection of LLMs and penetration testing to gain insight into their capabilities and challenges in the context of privilige escalation. We create an automated Linux privilege-escalation benchmark utilizing local virtual machines. We introduce an LLM-guided privilege-escalation tool designed for evaluating different LLMs and prompt strategies against our benchmark. We analyze the impact of different prompt designs, the benefits of in-context learning, and the advantages of offering high-level guidance to LLMs. We discuss challenging areas for LLMs, including maintaining focus during testing, coping with errors, and finally comparing them with both stochastic parrots as well as with human hackers.

翻译：渗透测试是网络安全的重要组成部分，它使组织能够主动发现并修复系统中的漏洞，从而加强其抵御潜在网络攻击的防御机制。渗透测试领域的最新进展之一是利用语言模型（LLM）。我们探索LLM与渗透测试的交叉领域，以深入了解它们在权限提升场景中的能力与挑战。我们利用本地虚拟机创建了一个自动化的Linux权限提升基准测试。我们引入了一种基于LLM的权限提升工具，旨在针对该基准测试评估不同LLM和提示策略。我们分析了不同提示设计的影响、上下文学习的优势以及向LLM提供高层次指导的益处。我们讨论了LLM面临的挑战领域，包括在测试中保持专注、应对错误，最终将其与随机鹦鹉及人类黑客进行比较。