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RAGSentinel: Certifiable Geometric Consensus for Robust Retrieval-Augmented Generation

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also exposes a critical security vulnerability: adversarial documents injected into the knowledge database can enter the context window and steer the model toward targeted incorrect answers. Existing post-retrieval defenses rely on instruction following, parametric knowledge, or text-level consistency, all of which can be imitated or optimized against by adaptive attackers. We propose RAGSentinel, a training-free, label-free defense for black-box RAG system

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