Adaptive Probe-based Steering for Robust LLM Jailbreaking

Abstract

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 act

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@misc{chen2026adaptive,
  title = {{Adaptive Probe-based Steering for Robust LLM Jailbreaking}},
  author = {Junxi Chen and Junhao Dong and Xiaohua Xie},
  year = {2026},
  month = may,
  eprint = {2605.20286},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.20286}
}