A Survey of Secure Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) improves large language models (LLMs) with external knowledge, but this access path creates security risks distinct from inherent prompt-only or parametric-model flaws. We frame secure RAG as securing external knowledge access. We conducted a systematic search and curated 135 works on attacks, defenses, and security evaluation, and organized them with SLOT: a taxonomy along the attack Surface (S) and the corresponding defense Layer (L), with cross-cutting axes Objective (O) and attack-target scope (T). Mapping these studies onto an external knowledge-access pipeline, we expose three mismatches: target mismatch (T2 attacks outpace T2 defenses and evalua...
| Authors | Yuming Xu, Mingtao Zhang, Zhuohan Ge et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 April 2026 |
| Last updated at source | 12 October 2026 |
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