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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.
AI summary 研究者提出一种轻量级路由 logits 安全检测器，在提示词预填充阶段读取 MoE 视觉语言模型的专家路由信号，在生成前判断多模态请求是否安全，且不修改模型参数或专家路由。
Published Oct 6
Evaluated models Qwen3-VL-30B-A3B-Instruct , Kimi-VL-A3B-Instruct Abstract 读出而非操纵 router logits，可在不改模型的情况下降低两模型的组合式安全错误。作者在两个指令微调 MoE VLM 上比较改模型与读路由。组合式风险按 HoliSafe 分成五类：图文各自可安全，合在一起才不安全，或不安全内容只在图像里。
提出融合定位拒答与能力保留的安全微调，补齐视觉定位输出的拒答缺口
Published Oct 5
Evaluated models Claude Sonnet 4.6 , Gemini 3.5 Flash , Molmo2-8B and others5 Abstract 论文揭示了 VLM 定位安全落后于 VQA 的对齐断层，提出三成分微调方案有效弥合差距并保留定位能力，表征分析揭示了对齐机制。
提出 Intent-Privilege OPSD，对齐视觉语言模型在隐式风险下的安全与帮助性
Published Sep 29
Evaluated models Qwen3-VL-4B-Instruct , LLaVA , LLaVA-1.5-7B and others4 Abstract 作者报告训练期证据化意图蒸馏兼顾安全与帮助性，效果受标注质量和评测设置影响。Intent-Privilege OPSD 是针对跨模态隐式风险的训练期安全—帮助性对齐方法。 作者关注的不是直接拒绝敏感请求，而是按可观察图文证据保留正常帮助、约束有害信息。
提出 AV-SafetyBench 评测文生音视频模型的不安全生成
Published Sep 7
Evaluated models LTX-2.3 , Ovi-1.1 , JavisDiT++ and others7 Abstract AV-SafetyBench 用三视角评五个 T2AV 模型，显示只看视频会漏掉音频与联合风险。AV-SafetyBench 是面向文本生成音视频（T2AV）的安全基准：一条提示词同时规定画面和音频，评测同时看两轨以及它们的交互。作者要测的是，不安全含义不只出现在画面上：它可以主要由语音或声音承载，也可以只有两轨合起来才成立。T2VSafetyBench 不评音频，TTA-Bench 不评与画面的交互，作者认为二者都盖不住这些路径。
Propose the KoNA benchmark to evaluate selective non-compliance in vision-language models
Published Sep 4
Evaluated models InternVL3-2B-Instruct , InternVL3-78B-Instruct , Qwen2.5-VL-3B-Instruct and others8 Abstract KoNA evaluates selective noncompliance with five types of paired queries.KoNA is used to evaluate whether vision-language models can selectively refuse mixed queries: only handle the components that should not be followed, while answering the remaining components.
审计视觉语言模型在敏感图像上从拒答转向国家立场重构的行为
Published Aug 12
Evaluated models Qwen2-VL-7B , Qwen2.5-VL-7B , Qwen3-VL-8B and others9 Abstract 论文系统审计了 VLM 多模态审查，发现审查正从显式拒答转向隐蔽重构，且中文提示与文本先验会放大重构倾向。论文系统研究了中外开源视觉语言模型在政治敏感图像上的审查行为，主要内容如下。
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