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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 AnchorRep，用 LoRA 推离有害提示表征以防御跨模型越狱迁移
Published Sep 26
Evaluated models Llama-3-8B , Mistral-7B , Vicuna-7B and others5 提出 Blindfold，用动作级操纵越狱具身 LLM
Published Mar 2
Evaluated models Llama-3.1-8B , DeepSeek-R1-14B , Gemma-3-27B and others8 Abstract Blindfold 以动作级代理规划越狱具身 LLM，作者称可绕过语义检查并在仿真与真机生效。Blindfold 是面向具身 LLM 规划模块的 no-box 攻击，把恶意意图改写成看似良性的动作序列。
Attacks arXiv 2503.01839 · Mar 4, 2025 提出 PromptTune，微调 LLM 改写提示以越狱文生图护栏
Published Mar 4, 2025
Evaluated models SDXL-Turbo , MACE , SafeGen Abstract LLM 改写可提高过滤器绕过率。PromptTune 用微调 LLM 改写提示词，攻击带安全过滤器或对齐的文生图模型。
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