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Oct 13 · Tuesday

All papers

Found 21 papers

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  1. DIBench:面向移动 GUI 智能体多候选选择决策完整性的安全基准

    用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性

    Published
    Evaluated models
    Qwen2.5-VL(32B), Qwen3-VL(32B), GUI-Owl(32B) and others7

    Abstract论文提出移动智能体决策完整性基准 DIBench,发现欺诈 UI 注入诱导目标选择并推高完成率,通用防御收益不稳定。

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  2. 窄域多模态微调可诱发涌现性失配,覆盖 15 个视觉语言模型

    证明无显式危害的图文窄微调可诱发跨任务涌现未对齐

    Published
    Evaluated models
    GPT-4o, GPT-4.1, Gemini 2.5 and others15
    Abstract证实窄多模态微调可引发全面的多渠道未对齐行为转变,揭示了后训练阶段对齐被隐蔽重塑的安全隐患。

    论文系统探究了视觉-语言模型中由窄任务微调诱发的跨模态涌现未对齐(Emergent Misalignment, EM)现象。

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  3. 通过稀疏自编码器特征追踪查询扩展效果

    用 SAE 追踪稠密检索查询扩展的潜变量并用激活转向验证

    Published
    Evaluated models
    Qwen3-Embedding-0.6B(Qwen-Embed), SimLM
    AbstractSAE 追踪稠密检索 QE 的潜变量,转向在四基准上比文本扩展更稳。

    作者用稀疏自编码器分析稠密检索里的查询扩展:现成检索器不微调时,拼接扩展文本往往增益有限甚至变差,而最终指标和嵌入整体偏移不足以说明内部发生了什么。

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  4. DefensesarXiv 2609.04767

    SHAQ: Privacy-preserving retrieval in vector databases using shadow queries

    Proposes SHAQ, which replaces document embeddings with shadow query embeddings to defend against embedding inversion.

    Published
    Attacker
    Black-box
    Evaluated models
    GTR-T5 encoder, DPR
    AbstractSHAQ replaces document embeddings with shadow query embeddings.

    SHAQ is a defense against EIA for cloud vector databases: instead of directly modifying document embeddings, it replaces them with shadow query embeddings.

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  5. Mind2Web-Injection:面向网页 Agent 视觉提示注入护栏的证据对齐评测

    提出 Mind2Web-Injection 评测网页 Agent 视觉注入护栏

    Published
    Evaluated models
    GPT-5.6-luna, Qwen3-VL-32B, Qwen3-VL-8B and others6
    Abstract作者称检测、定位和反事实响应在六个 VLM 上可分开。

    Mind2Web-Injection 用来检查网页截图护栏的判决是否对齐到该用的画面证据。。

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  6. 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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  7. 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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  8. 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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  9. DefensesarXiv 2603.14707

    Visual Confusion Agents: Exploiting and Defending Against Perception Failures in Computer-Use Agents

    Proposes out-of-process dual-channel comparison classification to intercept erroneous clicks by computer-use agents

    Published
    Evaluated models
    multi-modal-embed-small
    AbstractPerception errors can be exploited by swapping screen pixels.

    The authors frame CUA's perception failure as a security vulnerability, visual confused deputy: the agent authorizes click(x,y) based on a misread screen, and the actual landing point differs from the element it believes it is clicking.

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  10. DefensesarXiv 2602.10915

    Blind Gods and Broken Screens: Building a Secure, Intent-Centric Mobile Agent Operating System

    Proposes Aura, which uses a trusted kernel to mediate the identity, perception, and action control of mobile agents

    Published
    Evaluated models
    Gemini-3-Flash
    AbstractAura mediates structured interactions via the Agent Kernel.

    Aura uses kernel-mediated structured agent interaction, replacing screen-interface mobile assistants..

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  11. AttacksarXiv 2602.00364

    "Someone hid it": Query-agnostic black-box attacks against LLM retrieval

    提出查询无关黑盒攻击 DQ-A,用可迁移文末后缀降低文档检索排名

    Published
    Evaluated models
    Qwen-1.5-7B-Instruct, SFR-Embedding-Mistral, E5-Mistral-7B-Instruct and others10
    AbstractDQ-A 以无查询黑盒后缀压低 LLM 检索排名,并在 BRIGHT 上比较三种基线。

    DQ-A 是一种 query-agnostic、complete black-box 的文档隐藏攻击,对象是用最后隐层相似度匹配的 LLM 检索。

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