Automated Penetration Testing using Large Language Models

In the rapidly evolving field of cybersecurity, automated tools have become indispensable for identifying vulnerabilities and enhancing network security. Traditional penetration testing methods, while effective, often require extensive human expertise and can be time-consuming. This research introduces a groundbreaking system that integrates Large Language Models (LLMs), specifically the OpenHermes-2.5-Mistral-7B model, with automated penetration testing to revolutionize the security assessment process. This model automates the execution of penetration tests and provides AI-driven guidance, facilitating more efficient and comprehensive vulnerability assessments. Designed to operate across various environments including Kali Linux, Windows, and MacOS, and leveraging Python for scripting and automation, the model interprets context and user input to execute relevant security commands and adapt its testing strategies accordingly. This paper details the architecture, implementation, and operational capabilities of the model, demonstrating its effectiveness in simulating attack scenarios and identifying system vulnerabilities. Initial testing indicates that the model significantly reduces the time required for penetration testing while maintaining high standards of accuracy and thoroughness. The integration of LLMs not only enhances the automation of repetitive tasks but also introduces a new paradigm in adaptive testing based on real-time data analysis and decision-making. This study underscores the potential of LLMs to transform cybersecurity practices, setting a benchmark for future developments in automated security technologies.