WARP: A Word-Level Backdoor Attack Targeting RAG Systems via Retrieval Corpus Poisoning

Retrieval-Augmented Generation (RAG) systems retrieve relevant documents from a corpus database to mitigate issues like hallucination, outdated knowledge, and limited domain coverage. While enhancing large language models (LLMs) performance, RAG also introduces a new attack surface: adversaries can inject trigger-embedded malicious documents into the corpus database, potentially causing the LLM to produce attacker-controlled outputs.