Knowledge Poisoning: How Attackers Can Corrupt AI Systems via RAG Knowledge Bases

Retrieval-Augmented Generation (RAG) systems, which allow AI models to answer questions using external documents, are vulnerable to a threat called knowledge poisoning. Attackers can manipulate a RAG system's knowledge base by injecting false documents, hidden instructions, or irrelevant misleading text, causing the AI to produce incorrect or harmful outputs without directly targeting the model itself. Gradual poisoning is a subtler variant where small, incremental changes accumulate over time, making the source of misinformation difficult to trace. A developer named Rijul has built an open-source demo project illustrating these attacks in action using a Gemini-powered RAG setup. Defenses such as trusted-source whitelisting and pattern-based content checks can help filter out poisoned documents before they reach the model.