BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
BadRAG identifies security risks in Retrieval Augmented Generation (RAG) systems by poisoning external data sources. It demonstrates how attackers can insert malicious passages to create retrieval backdoors, leading to customized adversarial queries and influencing large language model outputs. The research shows that even small amounts of poisoned data can cause significant denial-of-service or sentiment steering attacks on LLMs.