English(EN) Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

新调查详细介绍了RAG的安全和隐私风险及防御措施

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-24 08:08

一篇新发布的arXiv调查论文详细介绍了与检索增强生成（RAG）系统相关的安全和隐私风险。该论文将威胁归类于各种RAG架构，包括集中式、设备端（Micro-RAG）和联邦模型。它概述了诸如成员推理、索引推理和投毒等攻击类别，同时还回顾了现有防御措施，并强调了隐私与效用之间的权衡。 AI

影响强调了RAG系统潜在的漏洞，这对于开发可信AI应用程序的开发人员至关重要。

排序理由该集群包含一篇在arXiv上发表的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Balamurugan Palanisamy, G S S Chalapathi, Vikas Hassija, Rajkumar Buyya · 2026-06-25 04:00

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

arXiv:2606.25533v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retrieval mechanisms with generative models, RAG systems improve factual grounding an…
arXiv cs.CL TIER_1 English(EN) · Rajkumar Buyya · 2026-06-24 08:08

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing large language models with external knowledge. By coupling retrieval mechanisms with generative models, RAG systems improve factual grounding and adaptability across domains. However, integratin…

报道来源 [2]

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems

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