MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks

arXiv:2502.17832v4 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its reliance on retrieval exposes MLLMs to knowledge poisoning attacks, in which adversaries deliberately inject malicious multimodal content into external knowledge bases to steer models toward generating incorrect or even harmful responses. We present MM-PoisonRAG, a framework to systematically study the vulnerability of multimodal RAG under knowledge poisoning. Specifically

Source: arXiv cs.LG — read the full report at the original publisher.

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