The Role of AI in Modern Penetration Testing
J. Alexander Curtis, Nasir U. Eisty

TL;DR
This paper systematically reviews how AI, especially Reinforcement Learning, is beginning to transform penetration testing by automating tasks and improving vulnerability detection, though real-world adoption and LLM applications are still limited.
Contribution
It provides a comprehensive overview of AI's current use in penetration testing, highlighting progress, challenges, and future research directions in this emerging field.
Findings
AI-assisted pentesting is in early stages but progressing.
Reinforcement Learning dominates current research efforts.
Real-world applications are limited but promising.
Abstract
Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there is a pressing need for more scalable and efficient testing methodologies. This systematic literature review examines how Artificial Intelligence (AI) is reshaping penetration testing, analyzing 58 peer-reviewed studies from major academic databases. Our findings reveal that while AI-assisted pentesting is still in its early stages, notable progress is underway, particularly through Reinforcement Learning (RL), which was the focus of 77% of the reviewed works. Most research centers on the discovery and exploitation phases of pentesting, where AI shows the greatest promise in automating repetitive tasks, optimizing attack strategies, and improving vulnerability identification. Real-world applications remain limited but encouraging, including…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Web Application Security Vulnerabilities
