Mitigating Database Leakage in RAG Systems with Keyword-Grounded Fact Substitution
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for combining large language models (LLMs) with external knowledge sources. However, RAG systems remain vulnerable to prompt injection attacks, which may mislead the retriever or generator to expose sensitive database contents. To address this issue, we propose KFS-RAG, a defense that mitigates information leakage by reformulating the retrieved context. Specifically, our method first identifies a small set of influential keywords from the retrieved context via an attention rollout plus a causal perturbation mechanism. These keywords are then used to guide an auxiliary LLM to generate a compact set of keyword-grounded ...
| Authors | Ziliang Zhang, Yubo Zhu, Wei Tong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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