PentHack: AI-Enabled Penetration Testing Platform for Knowledge Development

Abstract

The process of conducting and executing penetration testing within the pedagogical paradigm often requires complex and arduous processes. This is especially daunting for beginners who often struggle with the complexities of penetration processes: reconnaissance, enumeration, and system hacking. Research works to address this complexity leverage industry tools that have proven to work for industry-related training, however, they fail to support pedagogical learning in higher education systems. To address this limitation, this study proposed the development of an academic-focused penetration testing learning platform. The proposed approach integrates large language models (LLM) into the penetration testing lifecycle through a user-friendly GUI tool. The tool addresses the void in beginner-friendly ethical hacking tools by offering a stepwise guide, built-in commands and justifications, report generation, and an LLM prompt-engineered output displayed in a simple tabular format for easy reference. Furthermore, the tool provides an interactive menu for each phase of the penetration lifecycle thereby guiding users through common penetration testing commands. To cater to deeper learning needs, the tool leverages LLMs to furnish additional information on commands, empowering users with AI-generated insights. With the capability to compile a comprehensive report with all commands and logs acquired during its use, the proposed tool has the potential to reduce the time spent on research and decision-making. In addition, it streamlines the learning curve, allowing a more informed and structured approach to Pen-testing for beginners. By leveraging this platform, academics and learners can enhance their penetration testing knowledge without the complexities associated with learning penetration testing.