M2AC: Policy-Aware Access Control for Multimodal Retrieval-Augmented Generation Systems

To address the fine-grained access control problem for cross-modal data in multimodal Retrieval-Augmented Generation (RAG) systems, this paper proposes a policy-aware access control framework, M2AC (Multimodal Access Control). Centered on multimodal derived data such as text, images, OCR text, captions, summaries, and embedding vectors, this framework extends the traditional binary allow/deny decision into a policy control mechanism that covers the entire process of retrieval, reranking, fusion, summarization, citation, and generation. M2AC comprises two core components: the Derived Permission Propagation Engine (DPPE) and the Modality-Aware Policy Enforcer (MAPE). Experiments on a LangChain-based multimodal RAG with three vector databases, namely Milvus, Weaviate, and pgvector, show that M2AC's policy generation takes 48.3 seconds, on the same order of magnitude as recent RAG-specific access control methods while using the fewest rules; the F1 score of end-to-end policy enforcement reaches 96.8%, an improvement of 3.4 to 7.2 percentage points over these methods. Its average retrieval latency increases by only 4.7% compared to the lightest baseline, and its average defense success rate against four types of cross-modal attacks, such as OCR bypass, reaches 96.2%.