SafeRAG: A Benchmark for Evaluating Security Risks in Retrieval-Augmented Generation

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Retrieval-Augmented Generation (RAG): Focusing on Security Risks

Retrieval-Augmented Generation (RAG) has proven to be a promising method for extending the capabilities of large language models (LLMs) in knowledge-intensive tasks. By integrating external knowledge sources, LLMs can access information that was not included in the training process. However, this approach also carries security risks, as the integrity of the external data is not always guaranteed and manipulation is possible. A new benchmark called SafeRAG investigates these security vulnerabilities and assesses the susceptibility of RAG systems to data injection attacks.

SafeRAG: A Benchmark for RAG Security

SafeRAG was developed to systematically evaluate the security of RAG systems. The benchmark identifies four main attack surfaces: Noise, Conflict, Toxicity, and Denial-of-Service (DoS). For each of these categories, specific attack scenarios were developed and compiled into a dataset, the SafeRAG dataset. This dataset is used to simulate and quantify the impact of various attacks on the performance of RAG systems.

Attack Types and their Impacts

The attack types investigated in SafeRAG include "Silver Noise," "Inter-Context Conflict," "Soft Ad," and "White Denial-of-Service." "Silver Noise" refers to the injection of subtly incorrect information that is difficult to distinguish from correct data. "Inter-Context Conflict" describes the introduction of conflicting information from different sources. "Soft Ad" refers to the subtle placement of advertising within the retrieved information. "White Denial-of-Service" aims to cripple the RAG system by overloading it with irrelevant information.

Experiments with SafeRAG show that existing RAG systems are vulnerable to these attacks. Both the retriever component, which is responsible for selecting relevant information, and the filters, which are supposed to block harmful content, as well as the LLMs themselves, can be bypassed by the various attack methods. This leads to a deterioration in the quality of the generated content and, in the worst case, can lead to misinformation or misleading statements.

The Importance of SafeRAG for the Future of RAG

The results of SafeRAG highlight the need to give greater consideration to security aspects in the development and application of RAG systems. The identified vulnerabilities show that existing protective mechanisms are not sufficient to guarantee the integrity of the generated content. The development of more robust security measures is therefore essential to exploit the full potential of RAG while minimizing the risks of data manipulation.

SafeRAG provides valuable insights for research and development in the field of AI security. The benchmark makes it possible to systematically evaluate the security of RAG systems and uncover vulnerabilities. This contributes to the development of more robust and secure RAG systems and minimizes the risks of data injection attacks. For companies like Mindverse, which specialize in the development of AI-based solutions, these findings are of particular importance in order to develop secure and reliable AI applications for their customers.

Bibliography: Liang, X., Niu, S., Li, Z., Zhang, S., Wang, H., Xiong, F., Fan, J.Z., Tang, B., Song, S., Wang, M., & Yang, J. (2025). SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model. arXiv preprint arXiv:2501.18636. Liang, X., Niu, S., Li, Z., Zhang, S., Wang, H., Xiong, F., Fan, J.Z., Tang, B., Song, S., Wang, M., & Yang, J. (2025). SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model. arXiv. https://arxiv.org/html/2501.18636v1 ```