通过自适应调节增强探针引导攻击，无需手动调参即可提升大模型越狱效果。
Adaptive Probe-based Steering for Robust LLM Jailbreaking
- 基于对比激活统计自适应调整攻击强度
- 将有害性得分从6%提升至70%
- 无需额外提示词，适合研究模型安全漏洞
近期工作表明对比引导可实现大语言模型越狱。但现有方法依赖有限且固有偏见的对比提示，且需人工反复调试攻击强度，限制了其鲁棒性和有效性。本文利用模型提取思想，使学习到的引导向量逼近理想状态，并根据对比激活统计自适应调节引导强度。实验表明，该方法显著提升了探针引导攻击的有效性与鲁棒性，无需额外对比提示或繁琐人工调参。作为攻击类研究，本文揭示了加固后大模型的脆弱性，使平均有害性得分从6%提升至70%。代码已公开于https://github.com/fhdnskfbeuv/adaptiveSteering。
原文摘要 · Abstract (English)
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 tuning. Being an attack paper, this paper focuses on revealing the breakdown of fortified LLMs, raising the average harmfulness score from 6\% to 70\%. Our code is available at https://github.com/fhdnskfbeuv/adaptiveSteering.
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