Document Poisoning Threatens RAG System Security

A new security vulnerability has emerged targeting Retrieval-Augmented Generation (RAG) systems, a popular architecture where AI models fetch information from external documents to ground their answers. The threat, known as "document poisoning," involves attackers subtly injecting malicious, biased, or misleading data into the source materials a RAG system uses. Unlike direct attacks on the AI model itself, this method corrupts the knowledge base. When the AI retrieves information to answer a user's query, it unknowingly draws from this poisoned data, leading to manipulated, incorrect, or harmful outputs. This presents a stealthy and potent risk, as it can be difficult to detect and can undermine the reliability of enterprise AI applications in customer service, legal research, or internal knowledge management. The report highlights the need for robust data provenance, verification, and cleansing processes as RAG systems become more widespread.