RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications with Agent-based Attacks

Abstract: While LLMs have achieved notable success in generative tasks, they still face limitations, such as lacking up-to-date knowledge and producing hallucinations. Retrieval-Augmented Generation (RAG) enhances LLM performance by integrating external knowledge bases, providing additional context which significantly improves accuracy and knowledge coverage. However, building these external knowledge bases often requires substantial resources and may involve sensitive information. In this paper, we propose an agent-based automated privacy attack called RAG-Thief, which can extract a scalable amount of private data from the private database used in RAG applications. We conduct a systematic study on the privacy risks associated with RAG applications, revealing that the vulnerability of LLMs makes the private knowledge bases suffer significant privacy risks. Unlike previous manual attacks which rely on traditional prompt injection techniques, RAG-Thief starts with an initial adversarial query and learns from model responses, progressively generating new queries to extract as many chunks from the knowledge base as possible. Experimental results show that our RAG-Thief can extract over 70% information from the private knowledge bases within customized RAG applications deployed on local machines and real-world platforms, including OpenAI's GPTs and ByteDance's Coze. Our findings highlight the privacy vulnerabilities in current RAG applications and underscore the pressing need for stronger safeguards.

• The paper introduces RAG-Thief, an agent-based framework that iteratively refines queries to extract over 70% of private data from RAG systems.
• The methodology combines adversarial query generation with self-improvement mechanisms to outperform traditional prompt injection attacks threefold.
• The findings highlight critical privacy risks in RAG integrations, urging developers to implement stronger data safeguarding measures.

RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications

The paper "RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications with Agent-based Attacks" addresses the pressing issue of data privacy vulnerabilities in Retrieval-Augmented Generation (RAG) systems integrated with LLMs. RAG systems augment LLMs by integrating external knowledge bases to enhance the accuracy and knowledge coverage of LLMs. However, these knowledge bases often contain sensitive information, posing significant privacy risks. The authors propose an agent-based privacy attack, termed RAG-Thief, that systematically exploits these vulnerabilities to extract private data from RAG systems.

Technical Overview

RAG-Thief employs an innovative attack framework that combines initial adversarial queries with a self-improving mechanism. This approach iteratively refines queries based on previous model responses, significantly enhancing the scale of data extraction from private knowledge bases. The attack leverages an agent-based architecture that autonomously interacts with RAG applications, gradually expanding its knowledge extraction effectively. Unlike traditional prompt injection attacks, RAG-Thief automates the query generation process through a heuristic self-improvement mechanism, enabling it to efficiently extract over 70% of private knowledge base information in experimental scenarios.

Strong Results and Findings

The experimental evaluation of RAG-Thief reveals its formidable performance across multiple test settings involving local RAG systems and real-world platforms like OpenAI's GPTs and ByteDance's Coze. With respect to the chunk recovery rate (CRR), the method achieves a remarkable extraction rate of over 70%, significantly outperforming baseline methods by more than threefold. The semantic similarity and extended edit distance metrics further validate that RAG-Thief can closely reconstruct the original data, indicating a high fidelity in the extracted content, with minimal deviations often limited to punctuation variations.

Implications and Impact

The study sheds light on the inherent privacy vulnerabilities within current RAG systems, advocating for the necessity of enhanced data safeguarding strategies. The success of the RAG-Thief attack underscores an urgent need for RAG developers to adopt robust defensive measures, such as implementing strict keyword detection mechanisms and establishing optimal retrieval configurations to minimize unintended data exposure.

Possible Future Directions

While RAG-Thief demonstrates significant efficacy in current RAG systems, the paper acknowledges areas for further research. Future enhancements could involve integrating advanced generative models to improve reasoning capabilities, particularly in handling discontinuous or domain-specific knowledge bases. Exploring multi-modal reasoning frameworks could also augment the robustness and adaptability of attack mechanisms.

Conclusion

The research into RAG-Thief provides critical insights into the security vulnerabilities of RAG systems, presenting a sophisticated method to exploit these weaknesses systematically. By effectively reconstructing private data, this paper not only reveals the gravity of the privacy risks in RAG integrations but also offers a foundation for future protective strategies aimed at securing RAG applications. The combination of innovative adversarial techniques and agent-based automation in RAG-Thief points to a critical evolution in how privacy attacks on AI systems can be both conceptualized and executed.