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Oct 12 · Monday

All papers

Found 4 papers
Filter5

Attack type

Jailbreaks3Prompt injection18Poisoning and backdoors8Extraction and theft1

Attack technique

Gradient and representation optimization3Automated search9Multi-turn escalation6Encoding and obfuscation2Conversation structure manipulation2Cross-modal payloads3Defense-targeted5

System type

AI Agent3

Attacker access

Black-box3

Attack surface / entry point

User input4

Layer

Prompt layer46Application layer4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 262papers have no tags yet and are not included in filtered results.
Attack type：JailbreaksAttack type：Extraction and theftAttack technique：Encoding and obfuscationAttack technique：Conversation structure manipulationLayer：Application layerClear all

Another 262papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.04985

    研究揭示 Jev 作为应用决策层的安全与隐私风险

    评估应用决策层的输入注入与隐私推断风险

    arXiv
    2610.04985
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    Black-box, Gray-box
    Evaluated models
    Jev, NanoJev
    AbstractJev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。

    Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。

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  2. AttacksarXiv 2603.01414

    通过动作级操纵越狱具身 LLM

    提出 Blindfold，用动作级操纵越狱具身 LLM

    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 以动作级代理规划越狱具身 LLM，作者称可绕过语义检查并在仿真与真机生效。

    Blindfold 是面向具身 LLM 规划模块的 no-box 攻击，把恶意意图改写成看似良性的动作序列。

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  4. AttacksarXiv 2509.25624

    STAC：看似无害的工具调用链如何攻破 LLM Agent

    提出 STAC 框架，把恶意目标拆成多轮看似无害的工具调用链

    arXiv
    2509.25624
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Attacker
    Black-box
    Evaluated models
    GPT-4.1-2025-04-14, GPT-4.1-mini-2025-04-14, Qwen3-32B and others8

    Abstract作者提出并验证了智能体在序列化工具攻击下的普遍脆弱性，指出针对累积动作序列防御的必要性。

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