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Oct 13 · Tuesday

All papers

Found 6 papers
Filter4

Attack type

Jailbreaks6Prompt injection25Poisoning and backdoors13Detection evasion7

Attack technique

Automated search7Multi-turn escalation5Encoding and obfuscation2Defense-targeted5

System type

AI Agent6

Attacker access

Black-box3

Attack surface / entry point

User input6

Layer

Prompt layer40Application layer6

System component

Guardrails and judges3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.
Attack type：JailbreaksAttack technique：Encoding and obfuscationAttack technique：Defense-targetedLayer：Application layerClear all

Another 201papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.05943

    CRIME 框架利用良性 Agent 技能组合诱发恶意行为

    提出 CRIME 框架，组合良性 Agent 技能诱发恶意行为

    arXiv
    2610.05943
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    DeepSeek-V4-Flash, GPT-4o-mini, GPT-4o and others6
    Abstract良性技能经 DCA 或 ACA 组合后可产生恶意环境后果，单独审查对不上。

    CRIME 把公共技能库中各自通过审查的技能配成两两组合，看联合执行能否实现指定恶意任务。。

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  2. AttacksarXiv 2609.15383

    微软团队提出 capability laundering 攻击：弱模型拆分任务向对齐前沿模型套取能力

    提出能力洗钱攻击，拆分任务向对齐模型套取能力

    arXiv
    2609.15383
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Attacker
    Black-box
    Evaluated models
    GPT-5.5, Claude Opus 4.8, Grok-4.3 and others7

    Abstract作者提出了能力洗钱攻击，发现未对齐编排器分解咨询对齐模型可恢复能力差距，指出需建立跨会话防御。

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  3. AttacksarXiv 2606.09084

    研究者提出 Context-Fractured Decomposition 攻击

    提出 CFD 跨会话分解攻击，利用工件来源缺口绕过工具型 Agent 的本地检查

    arXiv
    2606.09084
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-4.1, GPT-5-nano, Claude-3.5-Sonnet and others6
    AbstractCFD 用无指令 artifact 把越狱拆到跨会话。

    CFD针对工具型 LLM 智能体：利用 artifact 来源不被跟踪这一部署失败模式，把有害目标拆开后延迟组合。

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  4. AttacksarXiv 2603.01414

    Jailbreaking embodied LLMs via action-level manipulation

    Propose Blindfold, jailbreaking embodied LLMs with action-level manipulation

    arXiv
    2603.01414
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics
    Attacker
    No-box (offline transfer)
    Evaluated models
    Llama-3.1-8B, DeepSeek-R1-14B, Gemma-3-27B and others8
    AbstractBlindfold jailbreaks embodied LLMs with action-level proxy planning, and the authors claim it can bypass semantic checks and is effective in both simulation and real-world settings.

    Blindfold is a no-box attack against embodied LLM planning modules, rewriting malicious intent into seemingly benign action sequences.

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