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Oct 12 · Monday
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.
评测文生图模型的有害内容生成能力与审核检测缺口
Published Oct 5
Evaluated models SDXL , Stable Diffusion 3.5 Large , FLUX.1-dev and others12 提出 FADE，对文生视频模型做帧感知多概念擦除
Published Oct 3
Evaluated models Wan2.1-T2V-1.3B , Wan2.1-T2V-14B , CogVideoX-2B and others4 Abstract FADE 用闭式编辑、帧感知适配器和软路由，在单骨干上擦除多概念并压低逐帧残差。FADE 是面向文生视频扩散 Transformer 的多概念遗忘方法，用来抑制指定物体、画风、裸体、暴力、仇恨和名人身份，并尽量保持其他生成。作者要处理两件事。与帧无关的抑制会让概念在少数帧重现，片段均值会掩盖这一失败。
提出 EraseSAE，在文生视频模型中局部擦除指定概念
Published Sep 3
Evaluated models CogVideoX-5B , HunyuanVideo , Flux.1 [dev] Abstract EraseSAE 用单义特征局部擦除。EraseSAE 把 SAE 用于 DiT 文生视频模型的概念擦除。作者要解决的是：全局改权重或表面负提示对不齐细粒度、分布式的概念，而且会改到无关时空区域。
Proposes CASG to resolve direction conflicts in multi-category safety guidance for text-to-image generation.
Published Feb 24
Evaluated models Stable Diffusion v1.5 , SDv3 提出 ChartAttack，用恶意提示驱动代码模型生成误导图表
Published Jan 19
Evaluated models Claude-4.6-Sonnet , GPT-4o , Gemini-2.5-Flash and others20 Abstract ChartAttack 生成不改数据的误导图，降低 MLLM 与人类的图表问答准确率。ChartAttack 研究恶意提示能否让 MLLM 在不改数据的情况下生成误导图表，并量化这些图对其他 MLLM 和人类图表问答的影响。
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