RAG Data Poisoning: Key Concepts Explained

Data poisoning is a significant security threat targeting AI systems, particularly those using Retrieval-Augmented Generation (RAG), by corrupting the external knowledge base these systems rely on for accurate information. This attack allows malicious actors to inject harmful content into databases, leading AI systems to generate incorrect or harmful outputs, which can have severe consequences in sectors like healthcare, finance, and security. Just a few strategically crafted documents can manipulate AI responses with high success, exploiting the AI's reliance on external context. The attacks can take various forms, such as instruction injection, context poisoning, and retrieval manipulation, often bypassing traditional security measures due to their sophisticated nature. The proliferation of RAG architecture introduces new vulnerabilities, including permission bypass, authentication gaps, and regulatory risks, as AI systems can inadvertently expose sensitive data. Real-world examples demonstrate the efficacy of these attacks, such as the Microsoft 365 Copilot exploit and ChatGPT memory poisoning, which show how attackers can gain unauthorized access and persistently exfiltrate data. Mitigation strategies include deterministic access control, input filtering, embedding analysis, and response filtering to detect and block malicious content, ensuring AI systems remain secure and reliable.