{"ID":3083847,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:32:54.120957816Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05787","arxiv_id":"2606.05787","title":"SentinelRAG: Synthetic Sentinel Knowledge for RAG Database Copyright Protection","abstract":"Protecting proprietary RAG databases from unauthorized redistribution is challenging: existing watermarking methods either inject fabricated relations between real entities, polluting the knowledge base with misinformation, or embed fragile lexical patterns that adversarial paraphrasing easily removes. We propose SentinelRAG, a watermarking framework that embeds style-consistent but fictitious knowledge entries into the RAG database. Our key insight is that synthetic knowledge describing fictitious entities is unlikely to be retrieved by legitimate queries, yet can be reliably triggered through targeted probes known only to the data owner. Experiments on four datasets ranging from 2.9k to 8.8M documents demonstrate that SentinelRAG achieves statistically significant detection $p \u003c 10^{-5}$ across all tested configurations at only a 0.1% injection rate. Compared to the state-of-the-art, our method significantly reduces the false detection rate while maintaining negligible interference with legitimate user queries.","short_abstract":"Protecting proprietary RAG databases from unauthorized redistribution is challenging: existing watermarking methods either inject fabricated relations between real entities, polluting the knowledge base with misinformation, or embed fragile lexical patterns that adversarial paraphrasing easily removes. We propose Senti...","url_abs":"https://arxiv.org/abs/2606.05787","url_pdf":"https://arxiv.org/pdf/2606.05787v1","authors":"[\"Tsun On Kwok\",\"Xi Yang\",\"Ki Sen Hung\",\"Chang Liu\",\"Yangqiu Song\"]","published":"2026-06-04T07:19:56Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}