This paper investigates the dynamics of jailbreak success in large language models (LLMs) by analyzing how different jailbreak types circumvent safety mechanisms. The study focuses on extracting and analyzing jailbreak vectors that can mitigate the effectiveness of other jailbreak types. The research reveals that jailbreak activations cluster based on semantic attack types, and that the most effective jailbreaks suppress the model's perception of prompt harmfulness. However, the reduction in perceived harmfulness does not apply equally to all jailbreak types, suggesting different mechanisms are at play. The study also explores the similarity and transferability of jailbreak vectors, finding that vectors extracted from one jailbreak type can mitigate the success of others. This suggests that jailbreak-mitigation approaches may be generalizable. The research further examines the harmfulness suppression mechanism, where jailbreaks reduce the model's perception of harmfulness, leading to successful jailbreaking. However, some jailbreaks, like disemvowel and leetspeak, show lower effectiveness despite their potential to suppress harmfulness. The findings indicate that while reducing harmfulness perception can lead to successful jailbreaking, there are other factors at play. The study also highlights the importance of understanding the interaction between harmfulness and helpfulness objectives in LLMs. The research contributes to the understanding of how jailbreaks function by analyzing activation dynamics and proposing a mechanism where jailbreaks reduce the model's perception of prompt harmfulness. The study's results have implications for developing more robust jailbreak countermeasures and improving the safety of LLMs. The research underscores the need for further investigation into the underlying processes that enable jailbreak success and the development of more effective safety mechanisms. The paper also includes a disclaimer noting that some examples may contain disturbing language.This paper investigates the dynamics of jailbreak success in large language models (LLMs) by analyzing how different jailbreak types circumvent safety mechanisms. The study focuses on extracting and analyzing jailbreak vectors that can mitigate the effectiveness of other jailbreak types. The research reveals that jailbreak activations cluster based on semantic attack types, and that the most effective jailbreaks suppress the model's perception of prompt harmfulness. However, the reduction in perceived harmfulness does not apply equally to all jailbreak types, suggesting different mechanisms are at play. The study also explores the similarity and transferability of jailbreak vectors, finding that vectors extracted from one jailbreak type can mitigate the success of others. This suggests that jailbreak-mitigation approaches may be generalizable. The research further examines the harmfulness suppression mechanism, where jailbreaks reduce the model's perception of harmfulness, leading to successful jailbreaking. However, some jailbreaks, like disemvowel and leetspeak, show lower effectiveness despite their potential to suppress harmfulness. The findings indicate that while reducing harmfulness perception can lead to successful jailbreaking, there are other factors at play. The study also highlights the importance of understanding the interaction between harmfulness and helpfulness objectives in LLMs. The research contributes to the understanding of how jailbreaks function by analyzing activation dynamics and proposing a mechanism where jailbreaks reduce the model's perception of prompt harmfulness. The study's results have implications for developing more robust jailbreak countermeasures and improving the safety of LLMs. The research underscores the need for further investigation into the underlying processes that enable jailbreak success and the development of more effective safety mechanisms. The paper also includes a disclaimer noting that some examples may contain disturbing language.