Title: ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Safety, Interpretability, Circuit, Multi-Head Attention, Scaling, Jailbreak Guard

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.