With the development of natural language processing (NLP), large language
models (LLMs) are becoming increasingly popular. LLMs are integrating more into
everyday life, raising public concerns about their security vulnerabilities.
Consequently, the security of large language models is becoming critically
important. Currently, the techniques for attacking and defending against LLMs
are continuously evolving. One significant method type of attack is the
jailbreak attack, which designed to evade model safety mechanisms and induce
the generation of inappropriate content. Existing jailbreak attacks primarily
rely on crafting inducement prompts for direct jailbreaks, which are less
effective against large models with robust filtering and high comprehension
abilities. Given the increasing demand for real-time capabilities in large
language models, real-time updates and iterations of new knowledge have become
essential. Retrieval-Augmented Generation (RAG), an advanced technique to
compensate for the model's lack of new knowledge, is gradually becoming
mainstream. As RAG enables the model to utilize external knowledge bases, it
provides a new avenue for jailbreak attacks.
In this paper, we conduct the first work to propose the concept of indirect
jailbreak and achieve Retrieval-Augmented Generation via LangChain. Building on
this, we further design a novel method of indirect jailbreak attack, termed
Poisoned-LangChain (PLC), which leverages a poisoned external knowledge base to
interact with large language models, thereby causing the large models to
generate malicious non-compliant dialogues.We tested this method on six
different large language models across three major categories of jailbreak
issues. The experiments demonstrate that PLC successfully implemented indirect
jailbreak attacks under three different scenarios, achieving success rates of
88.56%, 79.04%, and 82.69% respectively.

通过 LangChain 实现 Retrieval-Augmented Generation，我们提出了间接破解和一种新的间接破解攻击方法 ——Poisoned-LangChain (PLC)，该方法使用毒化的外部知识库与大型语言模型进行交互，从而导致大型模型生成恶意的非合规对话。在六个不同的大型语言模型上进行的实验表明，PLC 成功地实施了三种不同场景下的间接破解攻击，分别达到了 88.56％，79.04％和 82.69％的成功率。