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Oct 12 · Monday
All papers Found 55 papers Another 313 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.
构建 EpiReal-Bench,评测图像生成器将虚假主张渲染为可信证据
Published Oct 8
Evaluated models GPT-Image-2 , Nano Banana 2 , Grok Imagen 2.0 and others4 Abstract 四款商用图像模型能把假主张画成可信证据。EpiReal-Bench 用来量商用图像生成器会不会把虚假现实主张画成可信视觉证据。
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。
提出审计感知遗忘框架 AVCE,从文本与视觉通路擦除扩散模型概念
Published Oct 5
Evaluated models Stable Diffusion XL (SDXL) , Stable Diffusion v1.5 (SD v1.5) , Flux 1.0 Abstract AVCE 在审计锚点上改两条注意力通路。AVCE 是面向扩散模型的概念擦除方法,要让擦除经得起绕过文本条件的潜空间审计,而不只是挡住对抗提示词。
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。
遮挡审计 AdvWave-P 音频越狱的频带必要性与解码层关联
Published Oct 4
Evaluated models Qwen2-Audio-7B-Instruct , LLaMA-Omni Abstract AdvWave-P 的八频带排序与能量相关。这是对加性音频越狱 AdvWave-P 的频率与解码器深度审计,受害模型主要是 Qwen2-Audio-7B-Instruct,不提出新攻击。
提出 SceneJail,利用视频情境上下文越狱视频多模态模型
Published Sep 30
Evaluated models Qwen2.5-VL-7B-Instruct , Qwen2.5-VL-32B-Instruct , Qwen3-VL-8B-Instruct and others8 提出 CollageAttack,用空间文本碎片重组越狱文生图模型
Published Sep 29
Evaluated models GPT-Image-2 , Doubao-Seedream-5.0-Lite , Wan2.7-Image-Pro and others5 Abstract 系统揭示 T2I 跨模态空间合成安全漏洞,证实离散无害元素可在图像层面重构有害意图并放大传播强度。
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 RISE,迭代演化可复用策略对文生图模型做红队
Published Sep 28
Evaluated models DALL·E 3 , Nano Banana 2 , GPT-Image-2 and others7 提出 TempQ-Jail,以查询受限候选排序越狱文生视频模型
Published Sep 25 提出 AEGIS,用中层内部信号选择性激活后层适配器拦截音频越狱
Published Sep 24
Evaluated models Qwen2-Audio-7B , VITA-1.5 , Ultravox v0.5 and others6 提出 PALS,用潜空间平滑防御全双工语音对话的对抗扰动
Published Sep 23
Evaluated models Moshi-style agent 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,将有害意图拆成视觉锚点与运动文本以越狱视频生成模型
Published Sep 7
Evaluated models Wan , CogVideoX , LTX-Video and others9 Abstract DIVA 用视觉锚点抑制语义漂移做视频越狱,并给出多条件安全基准 TI2VSafetyBench。DIVA 是针对带参考图的视频生成模型的免训练 black-box 越狱:用一致性机制本身堵住语义漂移这条安全逃逸路径。 。
提出 AdvPIE,在黑盒下搜索表面无害却生成有害图像的提示
Published Sep 5
Evaluated models Stable Diffusion 3.5 Large , FLUX.1-schnell , Safe-StableDiffusion 1.5 and others5 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 CRACK,用多智能体辩论在黑盒下越狱文生图多层安全过滤
Published Sep 1
Evaluated models Stable Diffusion v1.4 , SDXL , DALL·E 3 and others4 Abstract CRACK 以分层诊断的多智能体辩论搜索多层 T2I 过滤器的联合规避区域。CRACK 是面向文本生成图像模型多层安全栈的黑盒越狱框架,用几何上的 Detection Surface 解释为何粗粒度反馈不够。
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 ARENA 音频红队框架,自动构造文本安全且音频接地的越狱输入
Published Aug 16
Evaluated models Audio Flamingo 3 , Qwen2-Audio-7B-Instruct , MiMo-Audio-7B-Instruct and others4