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Oct 12 · Monday
All papers Found 36 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.
提出 Safety-Aware Pruning，缓解视觉 token 剪枝放大的多模态越狱
Published Oct 7
Evaluated models LLaVA-1.5-7B Abstract 剪枝去掉背景 token 后注意力塌向恶意前景。这篇工作评估视觉 token 剪枝如何改变 VLM 在多模态越狱下的行为，并给出推理时防御 SAP。
固定有害指令，单轴归因图像到文本越狱的图像属性
Published Oct 4
Evaluated models GPT-4o , Claude Sonnet 4.5 , Qwen3-VL-8B-Instruct and others6 Abstract 固定文本的图像越狱拆解，只在 Qwen 上给出一条有界相关性归因。
构建 TriggerBench 并训练 ThinkingGuard，检测图文隐式风险
Published Sep 29
Evaluated models ThinkingGuard（Qwen3-VL-8B-Instruct） Abstract TriggerBench 隔离触发条件，ThinkingGuard 用分步归因在隐式风险检测上取得作者报告的较高平均分。这篇工作研究多模态隐式风险检测 ：图文各自良性，风险只在关键元素与触发元素交互时出现。
提出 IDUnlearn-Bench，评测 VLM 个体级遗忘后的身份残留
Published Sep 27
Evaluated models Qwen3-VL-2B , Qwen3-VL-4B , Qwen3-VL-8B and others6 Abstract IDUnlearn-Bench 用四族探针显示：压低直接属性访问后，身份仍常能被反向查出、绑定或重构。IDUnlearn-Bench 评测 VLM 是否忘掉一个可跨模态识别的人，而不只是答不出某条监督事实。
提出 QPriv-VL，用问题引导剪枝降低分布式视觉问答的隐私泄露
Published Sep 14
Evaluated models LLaVA-1.5-7B , Llama-2 Abstract QPriv-VL 在切层前按问题相关度与 DINOv2 敏感度剪视觉 token，作者称能在约四成 token 下兼顾准确率与四类攻击。QPriv-VL 把问题引导的视觉 token 剪枝做成分布式 VQA 的客户端隐私防御，而不是只做集中式推理加速。
提出 SPARK，在预填充阶段修复多模态 KV 记忆以防御视觉语言模型越狱
Published Sep 13
Evaluated models LLaVA-OneVision-7B , Chameleon-7B , Qwen2-VL-7B and others4 用后验重加权解释多模态上下文越狱，并提出风险门控的良性示例注入防御
Published Sep 8
Evaluated models Qwen3-VL-2B , Qwen3-VL-4B , Qwen3-VL-8B and others8 Abstract 后验重加权解释上下文越狱的缩放，并支持风险门控的良性示例防御。Zhang 等把多模态 ICL 越狱写成对齐模型在安全与有害隐模式之间的推理时后验漂移，并据此做风险门控防御。
提出 DIVA，利用跨步条件传播对离散扩散视觉语言模型实施视觉越狱
Published Sep 2
Evaluated models LLaDA-V , MMaDA , LLaDA2.0-Uni 提出 MAMJ，在元层面交替优化多模态越狱的策略提示与攻击者权重
Published Aug 27
Evaluated models GPT-4o , Gemini-3-Pro-Preview , Seed 2.0 提出 HACA，将图文越狱拆为原子策略并自动组合以诱导有害输出
Published Aug 3
Evaluated models LLaVA-1.6-Mistral-7B , InternVL-3.5-8B , VLGuard (LLaVA-v1.5-7B-Mixed) and others5 提出 INFER+ 信息过载方法，越狱大型视觉语言模型
Published Jul 3
Evaluated models Qwen3-VL-8B , Qwen2-VL-7B , InternVL3.5-8B and others8 Abstract 用图文过载越狱 LVLM。这篇工作用信息过载 越狱 LVLM，而不是靠 OOD 变换隐藏意图。
Propose MLingualFC, evaluating vision-language model jailbreak vulnerabilities with multilingual flowchart images
Published Jun 5
Evaluated models Qwen2.5-VL-3B-Instruct , Gemma-4-E4B-it , Pangea-7B 提出 ASO，用 GRPO 优化视觉风格以放大越狱攻击
Published Jun 1
Evaluated models GPT-4.1-mini , Gemini-2.5-Flash , Qwen3-VL and others6 Abstract 论文揭示了 MLLM 的风格敏感性脆弱面，提出 ASO 框架利用强化学习优化风格触发器，有效放大了现有视觉越狱效果。
提出 DSR 跨模态越狱，用无害图文诱导 MLLM 生成有害图像
Published Jun 1
Evaluated models gemini-2.5-flash-image , qwen-image-2.0 , Tongyi Wanx 2.5 Abstract DSR 用无害图文诱导商业 MLLM 输出有害图像，作者称组合能力会反过来扩大攻击面。DSR 是一种 black-box 跨模态越狱：输入侧不放显式有害内容，而让商业 MLLM 在推理中把无害图文重组成有害图像。
提出多图越狱框架 DMN，把有害意图拆进图像序列绕过安全护栏
Published May 18
Evaluated models GPT-4V , GPT-4o , GPT-5 and others10 提出 GPO-V，用全局概率优化的视觉扰动越狱扩散视觉语言模型
Published May 8
Evaluated models LLaDA-V , LaViDA Abstract 作者用视觉扰动改 dVLM 的全局生成概率，以绕过两类拒绝。GPO-V 把越狱从“开头必须顺从”改成“整段去噪概率朝肯定方向走”。 。
提出删字变体越狱方法，利用重构与隐藏权衡绕过多模态模型安全过滤
Published May 7
Evaluated models GPT-5.4-nano , GPT-5.4-mini , Gemini-2.5-Flash and others19 提出四类视觉越狱，把有害意图编码进图像以绕过安全拒答
Published May 1
Evaluated models GPT-5.2 , Claude-Haiku-4.5 , Gemini-3-Flash and others8 Abstract 四类视觉越狱在六个 VLM 上检验文本对齐能否挡住视觉意图，模型与攻击之间差异大。四类视觉越狱用来检验前沿 VLM 的文本安全训练能否挡住由图像传达的有害意图。 。
提出 VROP，把有害意图拆成良性视觉 gadget 越狱视觉语言模型
Published Mar 10
Evaluated models GPT-4o , Claude 3.7 Sonnet , GLM-4V-Plus and others7 Abstract VROP 以良性视觉 gadget 与控制流提示词做组合越狱。VROP 是 black-box 多模态越狱框架：不在输入中放显式恶意触发，而把有害目标拆成语义正交的良性视觉 gadget，再用控制流提示词把合成留到后期推理。
Propose StructAttack, which uses structural diagrams to induce visual models to assemble malicious content from harmless semantic slots.
Published Mar 8
Evaluated models GPT-4o (1120) , Gemini-2.0-Flash (001) , Gemini-2.5-Flash and others6 Abstract StructAttack decomposes a harmful query into harmless slots and draws them into a structure graph, inducing the LVLM to fill in harmful content under a black-box single query.StructAttack is a black-box, single-query jailbreak against LVLMs: it decomposes a harmful instruction into separate seemingly harmless slots, draws them into a structural diagram, and then makes the model reassemble the slots into harmful content.