Penetration Testing also referred to as Ethical Hacking, is a fundamental practice in Cybersecurity that involves evaluating the security of systems, networks, and applications to identify vulnerabilities and potential attacks. Traditional approaches to Penetration Testing are often time-consuming, relying on manual processes, and requiring advanced expertise due to the technical complexity of Penetration Testing tools. In this paper, we introduce a Retrieval-Augmented Generation (RAG) pipeline that enhances a trained large language model (LLM) to serve as an intelligent pentest assistant. We discuss the challenges involved in designing and implementing this architecture. It is designed to assist users throughout the Penetration Testing workflow, ranging from vulnerability assessment to remediation, by providing contextual guidance, command suggestions, and technical explanations in natural language. After implementing the proposed architecture, we found that combining the strengths of LLMs with the RAG pipeline notably improved the chatbot’s ability to assist users. It provided more accurate, relevant, and context-aware responses across various Penetration Testing tasks, making the interaction more effective and intuitive.
Fadhel et al. (Thu,) studied this question.