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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: 2511.06852v1

Added to Library: 2025-11-11 05:01 UTC

Red Teaming 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

  • The paper introduces a bi-directional model of LLM safety, deconstructing the refusal mechanism into distinct Harm Detection Direction and Refusal Execution Direction, which provides a deeper understanding of safety alignment.
  • Proposes the Differentiated Bi-Directional Intervention (DBDI) framework, a white-box intervention method that effectively neutralizes safety mechanisms in LLMs by applying adaptive projection nullification and direct steering techniques.
  • Demonstrates the efficacy of DBDI through extensive experiments, achieving up to a 97.88% attack success rate on models like Llama-2, significantly outperforming existing jailbreaking techniques.
  • Presents a rigorous analysis of intervention mechanisms, including layer selection, hyperparameter sensitivity, and the impact of sparsification on attack success rates, validating the robustness of the proposed methods.
  • Highlighting the ethical considerations of their framework, the authors acknowledge the potential misuse while advocating for awareness and proactive safety measures.

💡 Why This Paper Matters

This paper is highly relevant as it addresses critical vulnerabilities in LLM safety mechanisms by providing a new framework for understanding and potentially evading safety alignments. It contributes to the ongoing discourse on AI safety and robustness, directly addressing the risks posed by advanced LLMs and the exploits that can arise from their weaknesses.

🎯 Why It's Interesting for AI Security Researchers

This paper would interest AI security researchers due to its focus on jailbreaking methods, which expose critical flaws in LLM safety alignment. The novel techniques presented for evading these mechanisms contribute significantly to the field of AI security, offering insights into adversarial attacks and the defenses that can be developed against them.

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