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. While RAG has shown strong potential in reducing hallucinations and improving factual consistency, it also introduces new privacy and security challenges that differ from those faced by traditional LLMs. Existing research has demonstrated that LLMs can leak sensitive information through training data memorization or adversa
The rapid deployment of Retrieval-Augmented Generation (RAG) in various applications necessitates a formal understanding of its unique security and privacy vulnerabilities, distinguishing them from traditional LLM concerns.
This formalization provides a critical framework for identifying and mitigating security and privacy risks inherent in RAG systems, which are increasingly central to AI development and deployment.
The focus for AI security and privacy expands beyond LLM-specific issues to include the unique attack surfaces introduced by RAG's external document retrieval component, requiring new mitigation strategies.
- · AI security researchers
- · Organizations developing secure RAG applications
- · Cybersecurity solution providers
- · Developers neglecting RAG security
- · Users of insecure RAG systems
- · Organizations vulnerable to data breaches via RAG
Increased awareness and research into RAG-specific attack vectors.
Development and adoption of new security protocols and frameworks tailored for RAG systems.
Potential for regulatory guidance and compliance standards specifically addressing RAG security and privacy, impacting AI deployment speeds and costs.
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