Practical pen testing for GenAI, LLM and RAG apps
🛡️ This article outlines a practical, application-focused approach to penetration testing GenAI systems, emphasizing that prompts can be attack vectors. It recommends mapping architecture components (prompts, retrieval, embeddings, tools, APIs), defining strict rules of engagement and using canaries and synthetic data. Testers should treat prompt injection as multi-turn campaigns, evaluate RAG/vector stores and upstream ML pipelines, and automate repeatable attacks with controlled Python harnesses to preserve evidence and enable regression testing.
