Securing RAG: A Risk Assessment and Mitigation Framework

TL;DR

This paper reviews security vulnerabilities in Retrieval Augmented Generation (RAG) systems, identifies risks, and proposes a comprehensive framework combining RAG-specific and general security best practices to enhance safety and trustworthiness.

Contribution

It introduces a structured security assessment and mitigation framework tailored for RAG systems, integrating industry standards and best practices.

Findings

Identified key vulnerabilities in RAG pipelines.

Mapped risks to specific mitigation strategies.

Proposed a comprehensive security framework for RAG.

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.