# RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs

arXiv: https://arxiv.org/abs/2509.03768 · HTML: https://mlsanity.com/paper/2509.03768/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 4 of 4)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model](https://mlsanity.com/paper/2501.18636/) (2025)

## Built on by (most cited first, 5 of 5)

- [The Promptware Kill Chain: How Prompt Injections Gradually Evolved Into a Multistep Malware Delivery Mechanism](https://mlsanity.com/paper/2601.09625/) (2026)
- [Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering](https://mlsanity.com/paper/2607.00972/) (2026)
- [Evaluating Metrics for Safety with LLM-as-Judges](https://mlsanity.com/paper/2512.15617/) (2025)
- [Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution](https://mlsanity.com/paper/2606.25721/) (2026)
- [MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs](https://mlsanity.com/paper/2610.01058/) (2026)

## Abstract (arXiv)

Accuracy and safety are paramount in Offshore Wind (OSW) maintenance, yet conventional Large Language Models (LLMs) often fail when confronted with highly specialised or unexpected scenarios. We introduce RAGuard, an enhanced Retrieval-Augmented Generation (RAG) framework that explicitly integrates safety-critical documents alongside technical manuals.By issuing parallel queries to two indices and allocating separate retrieval budgets for knowledge and safety, RAGuard guarantees both technical depth and safety coverage. We further develop a SafetyClamp extension that fetches a larger candidate pool, "hard-clamping" exact slot guarantees to safety. We evaluate across sparse (BM25), dense (Dense Passage Retrieval) and hybrid retrieval paradigms, measuring Technical Recall@K and Safety Recall@K. Both proposed extensions of RAG show an increase in Safety Recall@K from almost 0% in RAG to more than 50% in RAGuard, while maintaining Technical Recall above 60%. These results demonstrate that RAGuard and SafetyClamp have the potential to establish a new standard for integrating safety assurance into LLM-powered decision support in critical maintenance contexts.