Adaptive Probe-based Steering for Robust LLM Jailbreaking
Recent work has demonstrated the potential of contrastive steering for jailbreaking Large Language Models (LLMs). However, existing methods rely on limited and inherently biased contrastive prompts and require laborious manual tuning of steering strength, limiting their robustness and effectiveness. In this paper, we leverage the idea of model extraction to guide the learned steering vectors to approximate the ideal one and propose tuning the steering strength adaptively based on contrastive activations' statistics. Experiments demonstrate that our method notably improves the effectiveness and robustness of probe-based steering, without any extra contrastive prompts or laborious manual tu...
| Authors | Junxi Chen, Junhao Dong, Xiaohua Xie |
|---|---|
| Primary category | cs.CR |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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