arXiv AI June 9, 2026 By Yuming Xu, Mingtao Zhang, Zhuohan Ge, Haoyang Li, Nicole Hu, Yongqi Zhang, Zhiyuan Wen, Jason Chen Zhang, Qing Li, Lei Chen

Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

Read the original on arXiv AI →

arXiv:2604. 08304v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conflates with inherent LLM flaws.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 28

TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

arXiv:2607. 23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time.

By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania

llms rag safety

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Jun 6

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

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.

By Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi, Kaushik Dutta

llms rag nlp safety

arXiv AI

Jul 2

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption

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.

By Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Xiaoyan Sun, Ping Chen, Jun Dai

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arXiv AI

Jun 18

Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems

arXiv:2606. 18310v1 Announce Type: cross Abstract: Injecting malicious knowledge into retrieval-augmented generation (RAG) systems can manipulate retrieved evidence and mislead downstream generation, posing a serious security threat for AI applications.

By Xinru Liu, Xianglong Zhang, Di Cai, Zhumin Chen, Pengfei Hu, Xin Xin

llms rag robotics benchmarks

arXiv Machine Learning

Jul 30

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

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.

By Pushkal Kumar, Tucker Nielson, Tanish Kolhe, Shubham Zala, Vincent Li

llms rag safety

arXiv AI

Jul 1

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems

arXiv:2606. 31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators.

By Seyed Bagher Hashemi Natanzi, Bo Tang

llms agents robotics safety

Related stories

TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption

Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems

RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems