Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
This addresses a security problem for users of RAG systems by revealing potential negative impacts on cognition and decision-making, though it is incremental as it builds on existing research on retrieval corruption attacks.
The paper tackles the vulnerability of Retrieval-Augmented Generation (RAG) models to black-box attacks for opinion manipulation, showing that the proposed attack strategy can significantly alter the opinion polarity of generated content, as demonstrated in experiments on opinion datasets across multiple topics.
Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retrieval corruption attacks. Existing research mainly explores the unreliability of RAG in white-box and closed-domain QA tasks. In this paper, we aim to reveal the vulnerabilities of Retrieval-Enhanced Generative (RAG) models when faced with black-box attacks for opinion manipulation. We explore the impact of such attacks on user cognition and decision-making, providing new insight to enhance the reliability and security of RAG models. We manipulate the ranking results of the retrieval model in RAG with instruction and use these results as data to train a surrogate model. By employing adversarial retrieval attack methods to the surrogate model, black-box transfer attacks on RAG are further realized. Experiments conducted on opinion datasets across multiple topics show that the proposed attack strategy can significantly alter the opinion polarity of the content generated by RAG. This demonstrates the model's vulnerability and, more importantly, reveals the potential negative impact on user cognition and decision-making, making it easier to mislead users into accepting incorrect or biased information.