Getting large language models (LLMs) to refuse to answer hostile toxicity
questions is a core issue under the theme of LLMs security. Previous approaches
have used prompts engineering to jailbreak LLMs and answer some toxicity
questions. These approaches can easily fail after the model manufacturer makes
additional fine-tuning to the model. To promote the further understanding of
model jailbreaking by researchers, we are inspired by Representation
Engineering to propose a jailbreaking method that does not require elaborate
construction prompts, is not affected by model fine-tuning, and can be widely
applied to any open-source LLMs in a pluggable manner. We have evaluated this
method on multiple mainstream LLMs on carefully supplemented toxicity datasets,
and the experimental results demonstrate the significant effectiveness of our
approach. After being surprised by some interesting jailbreaking cases, we did
extensive in-depth research to explore the techniques behind this method.

利用表示工程的思想，我们提出了一种无需精心构建提示，不受模型微调影响，并可以广泛应用于任何开源 LLMs 的越狱方法，通过在多个主流 LLMs 上进行评估，实验结果证明了我们方法的显著有效性，并对此方法背后的技术进行了广泛深入的研究。