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Oct 12 · Monday

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Found 3 papers
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Attack type

Jailbreaks3Poisoning and backdoors33Detection evasion17

Impact

Harmful content50Output manipulation3

System type

Models and APIs3

Layer

Prompt layer4Model layer3Training and data layer0
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Attack type：JailbreaksImpact：Output manipulationLayer：Model layerLayer：Training and data layerClear all

Another 232papers do not have category tags yet and are not included in filtered results.

  1. Analysis and theoryarXiv 2609.36477

    研究发现护栏模型在基座模型不确定处过度自信

    诊断护栏相对基座在对抗提示上的置信失校

    arXiv
    2609.36477
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-Guard 7B, Llama-Guard-2 8B, Llama-Guard-3 8B and others10
    Abstract对抗包装使 guard 高置信漏检，而对应 base 在同一输入上常更不确定。

    诊断性分析：五个提示词分类 guard 的置信度，在清洁输入上可以较校准，在越狱包装后则变成高置信度的错误判定。

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  3. Evaluation and benchmarksarXiv 2602.19450

    Red-teaming Claude Opus and ChatGPT security advisors for trusted execution environments

    Evaluating mechanism hallucination and prompt-induced failures in TEE advisors using TEE-REDBENCH

    arXiv
    2602.19450
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    ChatGPT-5.2, Claude Opus-4.6
    AbstractThe authors used TEE-REDBENCH to test two advisors, stating that failures are transferable and that pipeline controls can reduce their occurrence rate.

    TEE-REDBENCH red-teaming evaluation is used on ChatGPT-5.2 and Claude Opus-4.6 as security advisors for Intel SGX and Arm TrustZone, examining architecture reasoning, remote attestation semantics, mitigation suggestions, and refusals under policy constraints, and testing whether prompt-induced failures transfer across assistants. The authors treat the assistants as stochastic policies.

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