The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented
  Generation (RAG)
Shenglai Zeng Jiankun Zhang Pengfei He Yue Xing Yiding Liu Han Xu Jie Ren Shuaiqiang Wang Dawei Yin Yi Chang Jiliang Tang
Retrieval-augmented generation (RAG) is a powerful technique to facilitate
language model with proprietary and private data, where data privacy is a
pivotal concern. Whereas extensive research has demonstrated the privacy risks
of large language models (LLMs), the RAG technique could potentially reshape
the inherent behaviors of LLM generation, posing new privacy issues that are
currently under-explored. In this work, we conduct extensive empirical studies
with novel attack methods, which demonstrate the vulnerability of RAG systems
on leaking the private retrieval database. Despite the new risk brought by RAG
on the retrieval data, we further reveal that RAG can mitigate the leakage of
the LLMs' training data. Overall, we provide new insights in this paper for
privacy protection of retrieval-augmented LLMs, which benefit both LLMs and RAG
systems builders. Our code is available at
https://github.com/phycholosogy/RAG-privacy.