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Oct 13 · Tuesday

All papers

Found 27 papers

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  1. AttacksarXiv 2610.09240

    WebMirage hijacks visual web agents' browser operations with adversarial images

    提出 WebMirage,用局部对抗图像劫持视觉网页 Agent 的浏览器操作

    Published
    Setting
    web agent
    Evaluated models
    LLaVA-v1.5-13B, LLaVA-v1.6-34B, MiniCPM-o-8B and others6
    Abstract论文提出了针对网络智能体的端到端视觉红队框架 WebMirage,揭示了从视觉定位到浏览器执行的安全脆弱性。

    WebMirage 是一个面向视觉定位网络智能体的流水线感知端到端红队攻击框架。

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  2. AttacksarXiv 2610.09027

    P-VMI:对抗图像经 KV cache 持续影响多轮对话

    提出持久视觉记忆注入,使对抗图像经缓存在触发时操控输出

    Published
    Evaluated models
    Qwen3-8B, Qwen3-VL-8B-Instruct, Gemma-4-12B

    Abstract论文提出 P-VMI,证实对抗输入被掩蔽后仍可经 KV 缓存持久控制模型,强调了保留状态的安全风险。

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  3. AttacksarXiv 2610.05601

    Tracer 框架可从遗忘后的扩散模型中识别被擦除概念

    提出 TRACER,从权重差识别扩散模型被擦除概念

    Published
    Attacker
    White-box
    Evaluated models
    Stable Diffusion v1.5, Stable Diffusion 3, FLUX.1 and others5
    AbstractTRACER 用权重足迹识别未披露擦除概念并估计个数,作者称数秒内完成且准确率高于对照。

    TRACER 针对的是遗忘扩散模型上的目标识别:提供者从公开基座 W 用已发表方法擦掉未知集合 S⋆,只发布 W′。攻击者还有假定包含这些概念的词表,但不知擦了谁、擦了几个、用了什么算法。

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  4. AttacksarXiv 2609.35912

    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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  5. AttacksarXiv 2609.04533

    Repeat-After-Me:针对黑盒 VLM 的自适应视觉提示注入攻击

    提出 Repeat-After-Me 自适应视觉提示注入,诱导 VLM 智能体执行指定任务

    Published
    Setting
    AI Agent
    Evaluated models
    Claude-Opus-4.7, GPT-5.5, Gemini-3.1-Pro and others6

    Abstract论文提出了针对黑盒 VLM 的前缀诱导自适应视觉提示注入,在多款模型上诱发了工具调用与隐私泄露。

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  6. AttacksarXiv 2609.01723

    研究者提出针对微调 TTS 模型的黑盒成员推断攻击

    提出针对微调 TTS 的黑盒成员推断攻击

    Published
    Attacker
    Black-box
    Evaluated models
    CosyVoice2, F5-TTS, XTTS-v2
    Abstract黑盒 TTS 成员推断以 recitation 加分层语音表示为分数,说话人级强于记录级,DP-SGD 可压到随机附近。

    作者给出一个针对微调文本转语音模型的黑盒成员推断框架,分别审计说话人是否进入微调集,以及某一条文本–音频记录是否进入微调集。微调 TTS 以合成文本和参考语音双条件生成,既有生成模型 MIA 的单条件查询和图像/文本上的逐点比较不能直接套用。作者用 scorable extent 与 memorization elicitation 比较五种查询,认为只有 recitation(用目标转写和干净目标语音要求模型复现)同时满足两条,并把它作为主查询。

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  7. AttacksarXiv 2607.01276

    研究者提出嵌入推断攻击 EIA,可在黑盒 IR 系统中识别所用嵌入模型

    提出嵌入推断攻击 EIA,在黑盒 IR 中识别所用嵌入模型

    Published
    Attacker
    Black-box
    Evaluated models
    e5-small, e5-small-v2, e5-base and others19
    AbstractEIA 用无序文档集的差异从候选中识别嵌入模型,并给出 reranker 与阈值下的计数。

    EIA 是在黑盒 IR 上识别所用嵌入模型的攻击。。

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  8. AttacksarXiv 2606.12716

    PaperGuard:针对多模态 AI 同行评审的攻击与防御基准

    提出 PaperGuard,用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令

    Published
    Evaluated models
    Claude-sonnet-4.5, GPT-4o, GPT-4.1 and others11
    AbstractPaperGuard 测到提示注入与白盒视觉抬分,并比较分块检索防御。

    PaperGuard同时评测并检测多模态 AI 审稿中的评分操纵。

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  9. AttacksarXiv 2605.28074

    SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning

    提出 SilentRetrieval,以语义保持对抗文档投毒劫持 RAG 检索与生成

    Published
    Evaluated models
    Llama-2-7B-Chat, Mistral-7B-Instruct-v0.2, Qwen-7B-Chat and others10
    Abstract两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。

    SilentRetrieval 是针对 RAG 的两阶段语料投毒,在每查询一篇、合成目标答案的统一协议下,同时处理检索劫持、生成操纵和流畅度。

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  10. AttacksarXiv 2603.16960

    Adversarial attacks against modern vision-language models

    Conduct adversarial attacks on product images for open-source visual shopping agents and induce wrong purchases

    Published
    Setting
    web agent
    Evaluated models
    LLaVA-v1.5-7B, Qwen2.5-VL-7B
    AbstractThe authors state that LLaVA is easily induced by three attacks to buy the wrong item, while Qwen's correct purchase rate remains close to its clean condition.

    The authors compare the robustness of two open-source VLM shopping agents against image adversarial perturbations in a self-built e-commerce environment.

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  11. AttacksarXiv 2602.07379

    Aegis: Governance, Integrity, and Security for AI Voice Agents

    Proposes the Aegis red-teaming framework for multi-turn attacks on high-risk voice agents.

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-4o, GPT-4o-mini, Gemini-1.5 Pro and others7
    AbstractAegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.

    Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.

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  12. AttacksarXiv 2602.01757

    Zero2Text: Training-Free Cross-Domain Text Embedding Inversion Attack

    提出 Zero2Text,无需训练地跨域反演文本嵌入

    Published
    Attacker
    Black-box
    Evaluated models
    GTR-Base, Qwen3-Embedding-0.6B, Text-Embedding-3-small and others4
    Abstract零训练在线对齐可在未知域、无泄漏对时从黑盒 embedding 还原句子。

    Zero2Text 是一种不训练 decoder、也不使用泄漏文本–向量对的文本 embedding 反演,针对 RAG 向量库在严格黑盒、目标域未知时的还原。

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