AI-Augmented Ethical Hacking: A Practical Examination of Manual Exploitation and Privilege Escalation in Linux Environments

TL;DR

This paper investigates how generative AI can assist in manual exploitation and privilege escalation during Linux penetration testing, highlighting benefits, challenges, and the importance of human oversight.

Contribution

It provides a practical experimental analysis of GenAI's utility in ethical hacking tasks, emphasizing human-AI collaboration and addressing ethical concerns.

Findings

GenAI streamlines attack vector identification

Improves parsing of complex outputs for sensitive data

Highlights ethical challenges like data privacy and misuse

Abstract

This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments. Building on previous research into the role of GenAI in the ethical hacking lifecycle, this paper presents a hands-on experimental analysis conducted in a controlled virtual setup to evaluate the utility of GenAI in supporting these crucial, often manual, tasks. Our findings demonstrate that GenAI can streamline processes, such as identifying potential attack vectors and parsing complex outputs for sensitive data during privilege escalation. The study also identifies key benefits and challenges associated with GenAI, including enhanced efficiency and scalability, alongside ethical concerns related to data privacy, unintended discovery of vulnerabilities, and potential for misuse. This work contributes to the growing field of AI-assisted cybersecurity by emphasising the importance of human-AI collaboration, especially in contexts requiring careful decision-making, rather than the complete replacement of human input.