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NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models

Authors: Chuhan Zhang, Ye Zhang, Bowen Shi, Yuyou Gan, Tianyu Du, Shouling Ji, Dazhan Deng, Yingcai Wu

Published: 2025-09-04

arXiv ID: 2509.03985v1

Added to Library: 2025-09-05 04:00 UTC

Red Teaming

📄 Abstract

In deployment and application, large language models (LLMs) typically undergo safety alignment to prevent illegal and unethical outputs. However, the continuous advancement of jailbreak attack techniques, designed to bypass safety mechanisms with adversarial prompts, has placed increasing pressure on the security defenses of LLMs. Strengthening resistance to jailbreak attacks requires an in-depth understanding of the security mechanisms and vulnerabilities of LLMs. However, the vast number of parameters and complex structure of LLMs make analyzing security weaknesses from an internal perspective a challenging task. This paper presents NeuroBreak, a top-down jailbreak analysis system designed to analyze neuron-level safety mechanisms and mitigate vulnerabilities. We carefully design system requirements through collaboration with three experts in the field of AI security. The system provides a comprehensive analysis of various jailbreak attack methods. By incorporating layer-wise representation probing analysis, NeuroBreak offers a novel perspective on the model's decision-making process throughout its generation steps. Furthermore, the system supports the analysis of critical neurons from both semantic and functional perspectives, facilitating a deeper exploration of security mechanisms. We conduct quantitative evaluations and case studies to verify the effectiveness of our system, offering mechanistic insights for developing next-generation defense strategies against evolving jailbreak attacks.

🔍 Key Points

  • Introduction of NeuroBreak, a novel visual analytics system designed to analyze and mitigate jailbreak vulnerabilities in large language models (LLMs) at the neuron level.
  • Integration of probing techniques to assess and visualize layer-wise semantic changes and neuron functionality in relation to safety mechanisms.
  • Development of a multi-faceted neuron analysis framework that categorizes neurons into functional archetypes based on their roles in enabling or safeguarding against jailbreak attacks.
  • Quantitative evaluations demonstrate that NeuroBreak achieves significant security improvements while maintaining model utility, outperforming traditional fine-tuning methods on specific jailbreak attacks.
  • Case studies illustrate NeuroBreak's ability to systematically uncover vulnerabilities and reinforce defenses through targeted safety fine-tuning.

💡 Why This Paper Matters

The paper establishes NeuroBreak as an essential tool for enhancing the security of large language models against evolving jailbreak attacks. By providing deep insights into the internal mechanisms of LLMs, the system empowers researchers and practitioners to effectively diagnose and reinforce model defenses, ensuring the models adhere to ethical standards and resist adversarial exploitation.

🎯 Why It's Interesting for AI Security Researchers

This paper is highly relevant to AI security researchers as it addresses the pressing challenge of safeguarding large language models from jailbreak attacks—a growing concern in the deployment of AI systems. The novel methods proposed for visualizing internal model mechanics, along with the direct implications for enhancing security, present valuable avenues for ongoing research in AI safety and security frameworks.

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