Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However, the underlying retrieval databases may naturally contain, or be intentionally injected with, adversarial knowledge, which can perturb model outputs and undermine system reliability. To investigate this risk, prior studies have explored knowledge poisoning attacks in medical RAG systems. Nevertheless, most of them rely on the strong assumption that adversaries possess prior knowledge of user queries, which is unrealistic in deployments and substantially limits their practical applicability. In this paper, we pr...
| Authors | Peiru Yang, Haoran Zheng, Tong Ju et al. |
|---|---|
| Primary category | cs.CR |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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