Retrieval-Augmented Generative (RAG) models enhance Large Language Models
(LLMs) by integrating external knowledge bases, improving their performance in
applications like fact-checking and information searching. In this paper, we
demonstrate a security threat where adversaries can exploit the openness of
these knowledge bases by injecting deceptive content into the retrieval
database, intentionally changing the model's behavior. This threat is critical
as it mirrors real-world usage scenarios where RAG systems interact with
publicly accessible knowledge bases, such as web scrapings and user-contributed
data pools. To be more realistic, we target a realistic setting where the
adversary has no knowledge of users' queries, knowledge base data, and the LLM
parameters. We demonstrate that it is possible to exploit the model
successfully through crafted content uploads with access to the retriever. Our
findings emphasize an urgent need for security measures in the design and
deployment of RAG systems to prevent potential manipulation and ensure the
integrity of machine-generated content.

该研究论文探讨了检索增强的生成模型（RAG）如何整合外部知识库，提高其在事实核查和信息搜索等应用中的性能；同时指出了敌对方如何通过向检索数据库注入虚假内容来更改模型行为，进而对 RAG 系统进行成功的攻击，为此呼吁在设计和部署 RAG 系统时采取安全措施以确保机器生成内容的完整性。