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Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

Authors: Peng Zhang, Peijie Sun

Published: 2025-11-10

arXiv ID: 2511.06852v2

Added to Library: 2025-11-12 04:00 UTC

Safety

📄 Abstract

Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neural processes: the detection of harm and the execution of a refusal. In this work, we deconstruct this single representation into a Harm Detection Direction and a Refusal Execution Direction. Leveraging this fine-grained model, we introduce Differentiated Bi-Directional Intervention (DBDI), a new white-box framework that precisely neutralizes the safety alignment at critical layer. DBDI applies adaptive projection nullification to the refusal execution direction while suppressing the harm detection direction via direct steering. Extensive experiments demonstrate that DBDI outperforms prominent jailbreaking methods, achieving up to a 97.88\% attack success rate on models such as Llama-2. By providing a more granular and mechanistic framework, our work offers a new direction for the in-depth understanding of LLM safety alignment.

🔍 Key Points

  • Introduction of a bi-directional model of safety alignment in LLMs, distinguishing between Harm Detection and Refusal Execution pathways.
  • Development of the Differentiated Bi-Directional Intervention (DBDI) framework, which allows for targeted neutralization of safety mechanisms in LLMs.
  • Demonstration of DBDI's effectiveness with an attack success rate of up to 97.88% on various LLMs, significantly outperforming existing jailbreak methods.
  • Extensive experiments validating the necessity of a precise intervention strategy, including layer selection and intervention order for optimal efficacy.
  • Contribution to the understanding of LLM safety alignment as a composite of distinct neural processes, paving the way for improved safety mechanisms.

💡 Why This Paper Matters

This paper provides critical insights into the intricacies of LLM safety alignment, demonstrating that current models oversimplify the mechanisms at play. By deconstructing safety alignment into its fundamental components and proposing a novel intervention framework, the authors lay the groundwork for more robust defenses against jailbreak attacks. Their findings are not only theoretically significant but also practically applicable, as they highlight essential considerations for future AI safety research and applications.

🎯 Why It's Interesting for AI Security Researchers

This paper is of particular interest to AI security researchers as it addresses a pressing challenge in the deployment of LLMs: the vulnerability of these models to circumventing safety measures. By providing a deeper understanding of the mechanisms behind LLM safety and offering a method to both evaluate and exploit these vulnerabilities, the research sheds light on critical areas for further investigation and enhancement of AI safety protocols. Moreover, the results underscore the ongoing arms race between security measures and adversarial approaches, making it relevant for those involved in developing future protective mechanisms.

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