LLM-Based Intelligent Agents for Cybersecurity: A Tutorial and Survey of Automated Vulnerability Discovery

This paper provides a tutorial and survey of LLM-based agents for automated vulnerability discovery and penetration testing. The rapid advancements in Large Language Models (LLMs) have opened new possibilities for their application in cybersecurity. Unlike traditional tools, LLMs can process natural...

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Auteurs principaux:	Robel Addis, Kiran Porter, Jayden Ryan, Micah Stull, Shengjie Xu, Paul Wagner, Robert Honomichl, Steven Forsyth
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2026-01-01
Collection:	IEEE Access
Sujets:	Large language models (LLMs) AI agents cybersecurity autonomous penetration testing prompt engineering vulnerability discovery
Accès en ligne:	https://ieeexplore.ieee.org/document/11580353/
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