Abstract
This paper investigates the integration of Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) into Red Teaming exercises and penetration testing, addressing the growing complexity of cybersecurity threats and the limitations of traditional methods. Current Red Teaming practices often lack adaptability and efficiency in simulating sophisticated, evolving attack vectors, creating a gap in assessing real-world organizational vulnerabilities. This study introduces an AI-driven framework utilizing LLMs, such as ShellGPT, to enhance Red Teaming exercises by providing dynamic, context-aware suggestions across critical stages, from reconnaissance to exploitation, and even to the generation of the pentest report. Tested on a virtualized network mimicking real-world condition, LLMs demonstrated increased adaptability and improved the precision of attack simulations, enabling teams to emulate advanced adversarial techniques more effectively. The research also contributes a novel penetration testing model that integrates LLMs, addressing challenges such as operational scalability and sophisticated threat emulation. This approach not only elevates the quality of Red Teaming reports but also equips cybersecurity teams with advanced tools to anticipate and mitigate evolving threats. The findings underscore the transformative potential of GAI and LLMs in modernizing cybersecurity practices, ensuring they remain robust and aligned with the complexities of today’s threat landscape.
Published Version
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