RAG (Retrieval-Augmented Generation) poisoning from a document uploaded involves embedding malicious or misleading data into the source materials that an AI system uses for information retrieval and generation. In a RAG framework, the AI relies on external documents or databases to augment its responses, dynamically combining retrieved knowledge with its generative capabilities. By poisoning the document, an attacker can inject false information, bias, or harmful instructions into the retrieval pipeline, influencing the AI to produce distorted or harmful outputs. This attack exploits the trust placed in the uploaded document’s content and can be particularly dangerous if the AI system lacks robust validation mechanisms. Mitigating such risks requires implementing content sanitization, anomaly detection, and verification systems to ensure the integrity of uploaded documents and the responses they inform.