  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.
