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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。
评测 ReAct 工具调用对多模态模型拒答有害请求的削弱
Published Oct 3
Evaluated models Gemini-2.5-Pro , Gemini-3.1-Pro-Preview , Claude Opus 4.6 and others13 Abstract 论文揭示了工具调用范式削弱 MLLM 拒答能力的现象，分析了稀释与偏移机制并验证了重注缓解方案。论文系统评估了智能体工具调用范式对多模态大语言模型拒答能力的影响，指出了工具增强视觉推理背后的安全隐患。核心探讨的问题在于：当 MLLM 从单次推理转向通过 ReAct 循环调用外部工具（打标、缩放、OCR、代码执行）时，工具使用机制本身是否会降低模型拒答有害请求的概率。
测量并预测具身 VLM 规划器的任务拒答可变性
Published Sep 30
Evaluated models gemini-robotics-er-2-preview , gemini-robotics-er-1.6-preview Abstract 日常物体能翻转一部分已给出的拒答，可翻转程度取决于任务且可被本地代理预测。作者测量 constitution 守护的具身 VLM 规划器：一次拒答在只往场景里放一个日常物体之后还保不保持得住，并把这种易翻转程度称为任务的 malleability 。
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。
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