Walking the Embedding Space: Datastore Extraction from Multimodal RAG
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into relevant, up-to-date, external knowledge. Despite presenting several benefits, such as reducing hallucinatory behavior, they also introduce new attack surfaces, including leakage of private information and vulnerabilities against data extraction attacks. In this paper, we introduce $\immrag$, an adaptive and automatic data extraction attack procedure operating in a black box setting against \emph{image-returning} MRAG, a configuration in which the retrieved visual artifact is itself the response. E...
| Authors | Maria Carmen Jica, Ali Satvaty, Suzan Verberne et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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