How to Pentest LLMs Like a Security Researcher Cybersecurity

In this podcast, security researcher Darshan Naik discusses the fundamentals of LLM penetration testing, contrasting it with traditional web application pentesting. He explains the importance of reconnaissance, identifying whether a target is a real LLM or a static AI, and understanding the architecture of AI systems. The conversation covers common vulnerabilities such as prompt injection, hallucinations, excessive agency, and insecure integrations, often referencing the OWASP Top 10 for LLM applications. Naik demonstrates practical techniques, including embedding malicious prompts in images to bypass security controls, and emphasizes the need for proper validation, segmentation, and permission policies. He also highlights the role of MCP (Model Context Protocol) in AI workflows and how misconfigurations can lead to data exposure. The session includes walkthroughs of open-source labs for hands-on learning, and concludes with a discussion on how AI can be used to hack other AI systems. The content is aimed at security professionals and learners, providing a starting point for understanding and testing LLM security.