Rent‑a‑RAG introduces DirBucket, a provider‑side semantic watermarking system that embeds documents with meaning‑preserving paraphrases biased toward secret directions, allowing black‑box auditing of document reuse in multi‑provider retrieval‑augmented generation (RAG). The method consistently detects non‑compliance on a challenging benchmark, achieving strong target detection with no false positives and surviving adversarial post‑answer laundering. DirBucket’s detection transfers unchanged to real‑world corpora in clinical, cyber‑threat‑intelligence, and legal domains, demonstrating that embedding‑space watermarking can make third‑party RAG document reuse statistically auditable.
arXiv:2606. 11265v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious knowledge injection.
By Xi Nie, Hongwei Li, Shenghao Wu, Mingxuan Li, Jiachen Li, Wenbo Jiang
arXiv:2609.00470v1 Announce Type: new
Abstract: Retrieval-Augmented Generation (RAG) grounds large language models in external corpora, but implicit trust in retrieved documents creates a critical at...
By Muhaimin Bin Munir, Akib Jawad Ononto, Nazia Shehnaz Joynab, Bhavani Thuraisingham, Latifur Khan
arXiv:2502.10673v2 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. How...
By Yepeng Liu, Xuandong Zhao, Dawn Song, Yuheng Bu
arXiv:2606.17467v3 Announce Type: replace-cross
Abstract: Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and int...
By Aaditya Pai
RAGSentinel is a training‑free, label‑free defense designed for black‑box retrieval‑augmented generation systems. It employs a surrogate encoder to detect hidden‑state shifts caused by retrieved documents, removes shared topic directions, and filters poisoned documents as geometric outliers from a robust majority consensus. The method is proven to recover a poison‑free majority context under honest‑majority and representation‑separation assumptions, and experiments show it keeps attack success rates low while maintaining accuracy across multiple datasets, LLM families, and adaptive attacks.
By Yueyang Quan, Anjun Gao, Yufei Xia, Minghong Fang, Zhuqing Liu