ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack

• URL: http://arxiv.org/abs/2509.25843v1
• Date: Tue, 30 Sep 2025 06:33:52 GMT
• Title: ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack
• Authors: Yein Park, Jungwoo Park, Jaewoo Kang,
• Abstract summary: Large language models (LLMs) exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes.<n>In this work, we introduce Activation-Scaling Guard (ASGuard), an insightful, mechanistically-informed framework that surgically mitigates this specific vulnerability.
• Score: 22.48980625853356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes. As tense jailbreaking demonstrates that models refusing harmful requests often comply when rephrased in past tense, a critical generalization gap is revealed in current alignment methods whose underlying mechanisms are poorly understood. In this work, we introduce Activation-Scaling Guard (ASGuard), an insightful, mechanistically-informed framework that surgically mitigates this specific vulnerability. For the first step, we use circuit analysis to identify the specific attention heads causally linked to the targeted jailbreaking, the tense-changing attack. Second, we train a precise, channel-wise scaling vector to recalibrate the activation of tense vulnerable heads. Lastly, we apply it into a "preventative fine-tuning", forcing the model to learn a more robust refusal mechanism. Across three LLMs, ASGuard effectively reduces the attack success rate of targeted jailbreaking while preserving general capabilities and minimizing over refusal, achieving a Pareto-optimal balance between safety and utility. Our findings underscore how adversarial suffixes suppress the propagation of the refusal-mediating direction, based on mechanistic analysis. Furthermore, our work showcases how a deep understanding of model internals can be leveraged to develop practical, efficient, and targeted methods for adjusting model behavior, charting a course for more reliable and interpretable AI safety.

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