Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs

RAG (retrieval-augmented generation, where an AI pulls in external documents to answer questions) systems can be attacked by poisoning the documents they retrieve, which amplifies biases (systematic unfairness) in AI outputs even when the original question seems neutral. For example, a compromised RAG system might retrieve documents that show women as domestic violence victims, causing the AI to generate biased responses about gender. The paper demonstrates this attack using a framework called BRRA and shows that a dual stage defense mechanism can help reduce these attacks' impact.