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Oct 12 · Monday

All papers

Found 22 papers

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  1. MMSkillRisk:多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

    提出 MMSkillRisk 基准,评测多模态技能图像中的隐藏指令诱导 Agent 越权操作

    Published
    Evaluated models
    GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6

    Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击,揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。

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  2. AttacksarXiv 2609.07216

    DIVA:利用视觉锚点对视频生成模型实施多模态越狱

    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
    Attacker
    Black-box
    Evaluated models
    Wan, CogVideoX, LTX-Video and others9
    AbstractDIVA 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。。

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  3. SinoGlyphBench:中文字形混淆下语言模型审核的诊断基准

    提出 SinoGlyphBench,用配对字形混淆诊断中文审核稳定性

    Published
    Evaluated models
    GPT-5.4 mini, GPT-5.5, DeepSeek V4 Flash and others12
    AbstractSinoGlyphBench 显示中文字形混淆会同时增加有害漏报和误报,全文扰动最重。

    SinoGlyphBench是一个配对诊断基准,用来看内容审核在可还原的中文字形混淆下是否还保持原决定。

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  4. AttacksarXiv 2606.01837

    论文提出 DSR 跨模态越狱框架:用良性输入诱导 MLLM 有害输出

    提出 DSR 跨模态越狱,用无害图文诱导 MLLM 生成有害图像

    Published
    Attacker
    Black-box
    Evaluated models
    gemini-2.5-flash-image, qwen-image-2.0, Tongyi Wanx 2.5
    AbstractDSR 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。

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  5. AttacksarXiv 2605.00583

    Jailbreaking vision-language models through the visual modality

    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
    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。。

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  6. AttacksarXiv 2603.07590

    Model as Lego builder: assembling malicious content from harmless blocks using a semantic blueprint

    Propose StructAttack, which uses structural diagrams to induce visual models to assemble malicious content from harmless semantic slots.

    Published
    Attacker
    Black-box
    Evaluated models
    GPT-4o (1120), Gemini-2.0-Flash (001), Gemini-2.5-Flash and others6
    AbstractStructAttack 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.

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  7. AttacksarXiv 2603.00131

    Thought Virus:多 Agent 系统中潜意识提示引发的病毒式失准

    提出 Thought Virus,经潜意识提示使偏好沿多 Agent 消息传播

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
    AbstractThought Virus 把单点偏差传到下游。

    Thought Virus 把 subliminal prompting 放进多智能体通信,看单点偏差会不会在不点名目标概念时传到全网。

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  8. AttacksarXiv 2602.18514

    招聘中的特洛伊木马:标准模型与推理模型间接提示注入的红队测试案例研究

    用简历中的不可见指令做间接提示注入,操纵招聘推荐

    Published
    Evaluated models
    Qwen 3 30B A3B Instruct 2507, Qwen 3 30B A3B Thinking 2507

    Abstract作者称推理在简单注入下会帮攻击圆谎,在不合逻辑的复杂注入下又会把欺骗漏出来。

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  9. AttacksarXiv 2602.10148

    Red-Teaming Multimodal Reasoning: Cross-Modal Entanglement Attacks Jailbreak Vision-Language Models

    提出 COMET 跨模态纠缠攻击,越狱视觉语言模型

    Published
    Attacker
    Black-box
    Evaluated models
    GPT-4.1, GPT-4.1-mini, Gemini-2.5-Flash and others9
    AbstractCOMET 做黑盒跨模态纠缠越狱。

    COMET 是面向具备多模态推理的 VLM 的黑盒越狱,用来红队测试跨模态安全对齐。作者要处理的缺口是:把恶意线索分到图文的固定组合,测不到多步推理里才出现的漏洞。攻击不访问梯度,只构造图文输入。

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  10. AttacksarXiv 2601.21360

    Compliance Paradox: Semantic-Instruction Decoupling in Automated Academic Code Evaluation

    提出 AST-ASIP ,把对抗指令植入编译器忽略区域以劫持代码评分

    Published
    Evaluated models
    DeepSeek-V3.2, Qwen3-235B, GPT-OSS-120B and others9
    Abstract作者称 RLHF 的服从性使评分脱离代码。

    这篇工作把自动代码评分中的失败称为 Compliance Paradox:评分模型可以离开提交逻辑,转而执行嵌在可编译代码里的隐藏指令。

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  11. AttacksarXiv 2601.15698

    Beyond Visual Safety: Jailbreaking Multimodal Large Language Models to Generate Harmful Images with Semantically Irrelevant Inputs

    提出 BVS 图文越狱,用中和拼接图和诱导提示词让模型生成有害图像

    Published
    Evaluated models
    GPT-5 (12 January 2026 release), Gemini 1.5 Flash (15 January 2026 release)
    AbstractBVS 以重建后再生成诱导有害图像。

    BVS 用图文对诱导 MLLM 生成有害图像。作者认为,相对文本越狱和单模态图像诱导,视觉生成的安全审查仍不充分。

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