Conversational Large Language Models are trained to refuse to answer harmful
questions. However, emergent jailbreaking techniques can still elicit unsafe
outputs, presenting an ongoing challenge for model alignment. To better
understand how different jailbreak types circumvent safeguards, this paper
analyses model activations on different jailbreak inputs. We find that it is
possible to extract a jailbreak vector from a single class of jailbreaks that
works to mitigate jailbreak effectiveness from other classes. This may indicate
that different kinds of effective jailbreaks operate via similar internal
mechanisms. We investigate a potential common mechanism of harmfulness feature
suppression, and provide evidence for its existence by looking at the
harmfulness vector component. These findings offer actionable insights for
developing more robust jailbreak countermeasures and lay the groundwork for a
deeper, mechanistic understanding of jailbreak dynamics in language models.

对话式大型语言模型的研究中发现，监狱破解技术可以绕过模型的安全保障，通过分析模型对不同类型的监狱破解输入的激活情况，发现可以从一类监狱破解中提取出能够减少其他类监狱破解效果的监狱破解向量，而这或许意味着不同类型的有效的监狱破解通过相似的内部机制来实现，通过研究有害特征抑制可能的共同机制，提供有利于开发更强大的监狱破解对策的实证证据，为深入理解语言模型中监狱破解动态打下基础。