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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.
用词级归因指导同义词替换，翻转提示注入检测并验证越狱
Published Sep 22
Evaluated models Prompt Guard 2 (86M) , Llama 3.1 8B 提出 SinoGlyphBench，用配对字形混淆诊断中文审核稳定性
Published Sep 5
Evaluated models GPT-5.4 mini , GPT-5.5 , DeepSeek V4 Flash and others12 Abstract SinoGlyphBench 显示中文字形混淆会同时增加有害漏报和误报，全文扰动最重。SinoGlyphBench 是一个配对诊断基准，用来看内容审核在可还原的中文字形混淆下是否还保持原决定。
提出 Vaporizer，用词法变换、翻译和释义破解 LLM 输出水印
Published May 8 Extract system instructions with encoding and structured requests, and harden them by rewriting with CoT
Published Apr 1
Evaluated models GPT-4.1-mini , GPT-3.5-turbo , Gemini-2.5-flash and others7 Abstract Encoded requests can elicit system instructions after a direct refusal.This work evaluates and hardens the confidentiality of LLM system instructions under encoded extraction. The authors are concerned with: when direct extraction has been rejected, whether the model still surrenders protected content when the same request is rewritten as a formatting or encoding task.
提出面向渗出的权重压缩，评估推理服务器权重窃取可行性
Published Jan 4
Evaluated models Qwen2-1.5B , Qwen2-7B , Qwen2.5-1.5B and others8 Abstract 作者用残差量化加微调压缩权重，并称法医水印便宜且微调后仍可解码。这篇工作把 LLM 权重渗出看成 APT：攻击者已有未加密权重，但必须把完整权重藏进推理 API 的合法输出。作者认为可压缩性是被忽略的因素，因为推理量化要求解压后仍能低开销前向，而偷权重的人可以接受事后微调。
Attacks arXiv 2509.09112 · Sep 11, 2025 提出字符级扰动与遗传算法去除 LLM 水印
Published Sep 11, 2025
Evaluated models OPT-1.3B , LLaMA-3-8B Abstract 字符级扰动靠更大攻击范围移除水印。这篇工作评估推理时 LLM 水印在黑盒、有限查询下的移除难度，并比较字符级、token 级和句子级编辑。
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