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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 STCA，对驾驶视频 VLM 构造可迁移的时空对抗扰动
Published Oct 6
Evaluated models Video-LLaVA-7B , Qwen2.5-VL-7B , Dolphins 提出 FRA-Attack，用频域对齐与梯度正则做可迁移定向视觉攻击
Published May 20
Evaluated models GPT-5.2 , GPT-5.4 , Claude-Opus-4.6 and others15 Abstract FRA-Attack 以频域对齐和梯度低通做可迁移定向攻击，闭源面板上作者称整体更强，Gemma 除外。FRA-Attack 是面向闭源 MLLM 的迁移式定向图像攻击。作者要解决的是：空间域局部对齐重复全局低频，代理梯度的高频成分又把更新拉向代理自身。
Propose GDA, which aligns image adversarial perturbations with text-grounded evidence and transfers attacks to vision-language models
Published Feb 10
Evaluated models LLaVA , Qwen2.5-VL , InternVL3 and others10 Abstract GDA aligns perturbations with text-grounded evidence, and the authors say this improves zero-query black-box transferability on heterogeneous LVLMs.GDA is a zero-query black-box encoder transfer attack for heterogeneous LVLMs.
Attacks arXiv 2505.23266 · May 29, 2025 提出 AdvOF，生成对抗物体误导视觉语言导航智能体的场景感知
Published May 29, 2025
Evaluated models Lseg , Clip , ViT-L/16 and others5 Abstract AdvOF 以多视角融合生成对抗物体。AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。
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