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arXiv:2509. 20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses.
RAG-Safety-Bench is a benchmark designed to evaluate how retrieval-augmented generation (RAG) affects the safety of large language models (LLMs). It isolates safety impacts by testing four conditions: non-RAG, RAG with an oracle document, RAG with related but non-answer documents, and RAG with random safe documents. Results on five open-source LLMs reveal an inverse relationship between benign and unsafe capabilities, show that baseline safety guardrails do not guarantee safety in RAG, and confirm that even benign documents can trigger unsafe generation.
Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has demonstrated that retrieval-au...
arXiv:2607. 00012v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent knowledge limitations.
arXiv:2607. 26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence.
The paper surveys attacks and defenses in Retrieval-Augmented Generation (RAG), a technique that improves large language models by grounding outputs in external knowledge. It identifies new robustness and security risks such as corpus poisoning, backdoor attacks, privacy leakage, and fairness violations, and notes that existing surveys inadequately cover attacker objectives, threat models, and stage-specific defenses. The survey offers a unified, pipeline-aware overview, formalizing threat models across the corpus, retriever, and generator, categorizing attacks by accuracy, privacy, and fairness, and reviewing defenses for retrieval, rerank, generation, and traceback stages, while also summarizing robustness benchmarks and explainability methods.