Comprehensive Study on Integrating AI-Powered Threat Intelligence Using Large Language Models

This paper presents a comprehensive analysis of algorithmic efficiency within automated testing tools, with a particular emphasis on integrating AI-powered threat intelligence through Large Language Models (LLMs) for enhanced penetration testing. The investigation focuses on measuring the time complexity, resource utilization, and various performance metrics of these tools. We extend our analysis to explore how LLMs and multi-agent systems can augment dynamic and static analysis during Automated Penetration Testing (APT). We rigorously quantify the performance of both in-house developed tools and existing automation frameworks, especially those that leverage LLMs. The primary goal is to evaluate the strengths and weaknesses of the algorithm by comparing the performance of the tool, analyzing the underlying algorithms, and evaluating critical aspects such as scalability, resource management, and optimization strategies to improve real-world applications, particularly in cybersecurity.