RefineRAG: Word-Level Poisoning Attacks via Retriever-Guided Text Refinement

• URL: http://arxiv.org/abs/2604.07403v1
• Date: Wed, 08 Apr 2026 10:33:54 GMT
• Title: RefineRAG: Word-Level Poisoning Attacks via Retriever-Guided Text Refinement
• Abstract summary: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs)<n>Existing attack methods like PoisonedRAG remain detectable due to coarse-grained separate-and-concatenate strategies.<n>We propose RefineRAG, a novel framework that treats poisoning as a holistic word-level refinement problem.
• Score: 2.725904394644371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs), but simultaneously exposes a critical vulnerability to knowledge poisoning attacks. Existing attack methods like PoisonedRAG remain detectable due to coarse-grained separate-and-concatenate strategies. To bridge this gap, we propose RefineRAG, a novel framework that treats poisoning as a holistic word-level refinement problem. It operates in two stages: Macro Generation produces toxic seeds guaranteed to induce target answers, while Micro Refinement employs a retriever-in-the-loop optimization to maximize retrieval priority without compromising naturalness. Evaluations on NQ and MSMARCO demonstrate that RefineRAG achieves state-of-the-art effectiveness, securing a 90% Attack Success Rate on NQ, while registering the lowest grammar errors and repetition rates among all baselines. Crucially, our proxy-optimized attacks successfully transfer to black-box victim systems, highlighting a severe practical threat.

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