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Oct 12 · Monday
All papers Found 12 papers Another 233 papers do not have category tags yet and are not included in filtered results.
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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.
Propose KVeil, which adds perturbations to face videos to prevent unauthorized customization from reproducing identity
Published Oct 9
Evaluated models Wan2.2-TI2V-5B , HunyuanVideo-1.5 , Kling 3.0 and others4 Abstract KVeil adapts LETHE's reference-reading disruption to the challenge's two stages, ranking first on the leaderboard in both Final columns.
提出 UNTAG，绕过开源权重模型的 trigger-tag 滥用检测
Published Oct 2
Evaluated models Llama2 (7B) , Llama3 (8B) , Qwen2.5 (7B) Abstract 作者用 UNTAG 评估 trigger-tag，认为其不能充当开放权重滥用检测的主要边界。UNTAG 是针对开放权重 LLM 上条件滥用检测 trigger-tag 的统一攻击评估：信号可以在解码期注入，也可以写进权重，但攻击者若能改输出或改已发布权重，检测可以失效。
提出 ReGap，用自生成伪监督微调恢复模型中的隐私关联
Published Oct 1
Evaluated models GPT-2 Small , GPT-2 Medium , GPT-2 Large and others6 Abstract 提出无真实私有监督的恢复攻击 ReGap，实验显示微调可通过自生成数据唤醒模型潜在隐私关联，突显后适应隐私风险。
提出 GSU 在发布前封堵敏感门梯度，抵抗下游微调获取禁止能力
Published Sep 30
Evaluated models Llama-3.2-1B , Llama-3.2-3B , Llama-3.1-8B and others7 Abstract GSU 在发布前封堵 SiLU 门的获取梯度，并在 TOFU 与 WMDP 上降低下游微调得分。Gradient-Sealed Unlearning（GSU） 是一种发布前的 preemptive unlearning：不删除模型里已经有的能力，而是让指定禁止域能力更难被发布后的微调获取。
提出 PACT，遗忘推理模型在压力下的欺骗行为并保留上下文理解
Published Sep 30
Evaluated models DeepSeek-R1-Distill-Qwen-32B , QwQ-32B Abstract 论文将大模型欺骗形式化为上下文条件行为，提出 PACT 框架在消除欺骗的同时保留了上下文遵循能力。
检验层冻结与奇异方向截断能否挡住少样本有害微调
Published Sep 29
Evaluated models Llama-3.1-8B-Instruct , Llama-3.1-Tulu-3-8B-DPO , OLMo-2-1124-7B-Instruct and others10 Abstract 定位与恢复可以复现，但知情攻击能把损伤移出冻结带，并打败跨检查点的截断修复。这篇工作检验：把拒答定位到某些层或更新方向之后，能不能在攻击者知情时还守住拒答。 。
提出跨架构攻击 CSR，用概念分数参数重学习恢复文生图被擦除概念
Published Sep 27
Evaluated models FLUX.1-dev , Stable Diffusion-v1.4 提出 ARIA，在冻结权重上用 SAE 做推理时遗忘门控
Published Sep 15
Evaluated models DeepSeek-R1-Distill-Qwen-1.5B , Gemma-3-1B-it 提出 USG，在部分保护开源权重发布时阻断有害微调梯度
Published Aug 6
Evaluated models Qwen3-14B（表中作 Qwen-14B） , Llama-3.1-8B（表中作 Llama-8B） Abstract USG 在 PPOW 下用零空间立方层阻断有害微调梯度。Unidirectional Safety Gate 是面向 PPOW 发布设定的发布时防护：大部分权重仍可训练，一小部分安全关键组件由提供者侧加固保留，下游不必配合额外安全流程。 。
提出 GeoDetect 以几何分数检测视觉语言预训练模型的对抗样本
Published Jul 16
Evaluated models CLIPViT (ViT-B/16) , CLIPCNN (ResNet-50) , ALBEF and others4 Abstract GeoDetect 用几何分数检测 VLP 对抗样本。GeoDetect 是面向视觉-语言预训练模型的对抗样本检测方法，用嵌入几何而不是任务 logits 区分干净样本与对抗样本。
提出 CFA2，用 SAE 定位防御方向并正交化输出权重实现越狱
Published Feb 5
Evaluated models Llama 3.1 8B Instruct , Llama2 7B Chat , Mistral 7B Instruct and others4 提出 CORVUS，用 LoRA 伪装内部遥测以绕过幻觉检测器
Published Jan 19
Evaluated models Llama-2-7B-Chat , Vicuna-7B (v1.5) , Llama-3-8B-Instruct and others4 Abstract 作者称内部遥测在自适应模型侧对抗下易被操纵，CORVUS 通过轻量适配器微调降低了多种幻觉检测器的效果。
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