RAGSentinel: Certifiable Geometric Consensus for Robust Retrieval-Augmented Generation
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 systems. RAGSentinel uses a surrogate encoder to measure query-conditioned hidden-state shifts induced ...
| Authors | Yueyang Quan, Anjun Gao, Yufei Xia et al. |
|---|---|
| Primary category | cs.CR |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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