SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of
  Large Language Model

Xun Liang; Simin Niu; Zhiyu Li; Sensen Zhang; Hanyu Wang; Feiyu Xiong; Jason Zhaoxin Fan; Bo Tang; Shichao Song; Mengwei Wang; Jiawei Yang

Paper · arXiv 2501.18636

SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model

Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Jason Zhaoxin Fan, Bo Tang, Shichao Song, Mengwei Wang, Jiawei Yang

30 upvotesJanuary 28, 2025arXiv 预印本

AI 摘要

The SafeRAG benchmark evaluates the security of RAG systems by simulating various attack scenarios, revealing significant vulnerabilities in RAG components.

retrieval-augmented generationRAGlarge language modelsexternal knowledgesilver noiseinter-context conflictsoft adwhite Denial-of-ServiceRAG security evaluation datasetretrieversfilters

Abstract

The indexing-retrieval-generation paradigm of retrieval-augmented generation (RAG) has been highly successful in solving knowledge-intensive tasks by integrating external knowledge into large language models (LLMs). However, the incorporation of external and unverified knowledge increases the vulnerability of LLMs because attackers can perform attack tasks by manipulating knowledge. In this paper, we introduce a benchmark named SafeRAG designed to evaluate the RAG security. First, we classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service. Next, we construct RAG security evaluation dataset (i.e., SafeRAG dataset) primarily manually for each task. We then utilize the SafeRAG dataset to simulate various attack scenarios that RAG may encounter. Experiments conducted on 14 representative RAG components demonstrate that RAG exhibits significant vulnerability to all attack tasks and even the most apparent attack task can easily bypass existing retrievers, filters, or advanced LLMs, resulting in the degradation of RAG service quality. Code is available at: https://github.com/IAAR-Shanghai/SafeRAG.