Securing RAG: A Risk Assessment and Mitigation Framework

2025-05-13

AI Summary

Integrating sensitive data into Retrieval-Augmented Generation (RAG) systems introduces novel security and privacy risks across the end-to-end pipeline—spanning data preprocessing, storage, retrieval, and large language model (LLM) generation. Method: We systematically identify and characterize the full RAG attack surface, proposing the first RAG-specific attack surface definition methodology. Our structured governance framework integrates domain-specific RAG properties with established standards—including ISO/IEC 27001 and NIST SP 800-53—enabling bidirectional mapping between security controls and compliance requirements. We conduct rigorous risk modeling, attack tree analysis, and RAG pipeline security auditing. Contribution/Results: The work yields a practical, actionable security checklist, a mitigation strategy matrix, and an implementation guide. It directly supports enterprise RAG deployments in achieving compliance with China’s Multi-Level Protection Scheme (MLPS) Level 3 and the GDPR—thereby bridging critical theoretical and practical gaps in RAG security governance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Application Domains: Security
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Retrieval Augmented Generation (RAG) has emerged as the de facto industry standard for user-facing NLP applications, offering the ability to integrate data without re-training or fine-tuning Large Language Models (LLMs). This capability enhances the quality and accuracy of responses but also introduces novel security and privacy challenges, particularly when sensitive data is integrated. With the rapid adoption of RAG, securing data and services has become a critical priority. This paper first reviews the vulnerabilities of RAG pipelines, and outlines the attack surface from data pre-processing and data storage management to integration with LLMs. The identified risks are then paired with corresponding mitigations in a structured overview. In a second step, the paper develops a framework that combines RAG-specific security considerations, with existing general security guidelines, industry standards, and best practices. The proposed framework aims to guide the implementation of robust, compliant, secure, and trustworthy RAG systems.

Problem

• Identify vulnerabilities in RAG pipelines and their attack surfaces
• Propose mitigations for security and privacy risks in RAG systems
• Develop a framework for secure and compliant RAG implementation

Innovation

• Assesses RAG pipeline vulnerabilities comprehensively
• Proposes mitigations for identified security risks
• Combines RAG-specific and general security guidelines