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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.
提出审计感知遗忘框架 AVCE，从文本与视觉通路擦除扩散模型概念
Published Oct 5
Evaluated models Stable Diffusion XL (SDXL) , Stable Diffusion v1.5 (SD v1.5) , Flux 1.0 Abstract AVCE 在审计锚点上改两条注意力通路。AVCE 是面向扩散模型的概念擦除方法，要让擦除经得起绕过文本条件的潜空间审计，而不只是挡住对抗提示词。
提出 Target-free LAT，用无目标多方向潜干预做对抗安全对齐以抵御越狱
Published Oct 3
Evaluated models Qwen2.5-7B-Instruct , Llama-3-8B-Instruct , Mistral-7B-Instruct-v0.2 Abstract Target-free LAT 用无目标多方向潜干预扩大行为覆盖，作者称能提高越狱鲁棒性并保住效用。Target-free LAT 是一种不监督有害输出的潜空间对抗对齐。它要处理的问题是：现有 LAT 每步大多只造一种有害行为，对不上真实越狱里的直接遵从、角色扮演、编码和多语言等模式。
提出 Whiteout，用伪装样本微调覆盖个人敏感信息关联
Published Oct 1
Evaluated models Llama 3.2 , Phi-3-mini , Gemma 3 and others7 提出 SafeMol，联合对齐分子多模态模型的文本与图输入
Published Sep 27
Evaluated models Omni-Mol v2 , ReactXT , SafeMol Abstract SafeMol 用 MMD 和双分类头对齐分子多模态模型，在 Omni-Mol v2 上压低图条件攻击成功。SafeMol 研究分子多模态模型在危险分子相关请求上的安全对齐，并同时看过拒和任务效用。
提出 RAPID，离线把抗蒸馏扰动写入文生图解码器
Published Sep 15
Evaluated models PixArt , SSD , SDXL and others4 提出 ReSO ，对齐潜在道德表征以提升越狱条件下的安全泛化
Published Sep 4
Evaluated models Qwen3-8B , Qwen3-14B , Qwen3-32B and others17 Abstract ReSO 重组道德分类的潜在关系。ReSO 把对齐目标从偏好回答改成道德分类的潜在关系。 。
提出 SEAL，在 MoE 共享专家上做 DPO-LoRA 对齐以抵御越狱与权重篡改
Published Sep 2
Evaluated models Qwen1.5-MoE-A2.7B , DeepSeekMoE-16B , GLM-4.7-Flash and others4 Abstract SEAL 把 DPO-LoRA 限制在混合 MoE 共享专家上，SEAL++再保护已有安全神经元方向。SEAL 是面向混合 MoE 的训练时防御：只对齐始终执行的共享专家，使安全不依赖路由器是否把 token 送到安全专家。
把批量提示当作越狱攻击，提出 batch-aware DPO 缓解
Published Aug 3
Evaluated models Llama-3.1-8B , Phi-4-14B , Qwen-3-8B and others6 Abstract 有害问题混入良性 batch 可引出有害回答。这篇工作检查一种本用于省推理成本的输入结构：把多个问题一次送进对齐后的 LLM，安全拒答是否还和单问时一样。
提出 CodeSpear 越狱，借语法约束解码生成恶意代码，并给出 CodeShield 防御
Published Jun 10
Evaluated models Qwen2.5-Coder-7B , Qwen2.5-Coder-32B , Qwen2.5-7B and others10 Abstract 论文发现语法约束解码会破坏自然语言安全对齐，提出 CodeSpear 越狱攻击与基于蜜罐代码对齐的 CodeShield 防御。
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