SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generation
This work addresses the vulnerability of existing Retrieval-Augmented Generation (RAG) systems to sensitive information leakage during retrieval and their susceptibility to prompt injection attacks due to reliance on prompt-based privacy mechanisms. To mitigate these issues, the authors propose SD-RAG, a novel framework that shifts security and privacy controls to the retrieval stage, applying semantic-level filtering and selective disclosure before data reaches the large language model. SD-RAG innovatively decouples policy enforcement from text generation by introducing dynamically interpretable security policies, a semantics-driven policy parsing mechanism, and a fine-grained, policy-aware graph-based retrieval model. Experimental results demonstrate that SD-RAG improves privacy protection scores by up to 58% over baseline methods while significantly enhancing robustness against prompt injection attacks.