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Oct 13 · Tuesday

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Found 6 papers
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Jailbreaks6

Impact

Harmful content3

System type

Models and APIs6

Attack surface / entry point

User input6

Layer

Prompt layer36Model layer6Application layer11
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  1. Evaluation and benchmarksarXiv 2610.10276

    PatchBench 提出激活补丁局部评测协议，揭示越狱修复的附带损伤

    提出 PatchBench-Local 协议，评测激活补丁修复越狱的局部附带损伤

    arXiv
    2610.10276
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2.5-3B-Instruct, Qwen2.5-14B-Instruct, gemma-3-4b-it and others8

    Abstract论文提出 PatchBench 与局部评估协议，揭示了激活补丁在全局能力无损下仍存局部良性退化。

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  3. Evaluation and benchmarksarXiv 2608.28233

    REINS：用 SAE 特征同时抑制有害续写并增强拒答的引导方法

    提出 REINS，在 SAE 空间抑制有害延续并增强拒答

    arXiv
    2608.28233
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3.5-4B-Base, Qwen3.5-2B-Base, Gemma-3-1B-IT
    AbstractREINS 分角色抑制有害延续并增强拒答。

    REINS 是一种推理时 SAE 安全转向方法，并配有用于上下文伪装的基准 GUISE。

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  4. Evaluation and benchmarksarXiv 2608.20554

    aiXamine：跨维度评测 LLM 安全、安全（security）与隐私权衡

    提出 aiXamine 黑盒基准，统一评测 LLM 安全、稳健与隐私权衡

    arXiv
    2608.20554
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    openai/gpt-5-mini, openai/gpt-4o, anthropic/claude-haiku-4.5 and others15
    AbstractaiXamine 用九服务黑盒协议比较百余个模型，作者称单轴信任度进展可能削弱其他轴。

    aiXamine 是一套黑盒评测协议，用来在同一条件下比较 LLM 的 safety、security 与 privacy，而不是再产出一个能力分。

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  5. 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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  6. Evaluation and benchmarksarXiv 2602.14161

    When benchmarks lie: evaluating malicious prompt classifiers under real-world distribution shift

    提出 LODO 评测恶意提示探针的跨数据集泛化

    arXiv
    2602.14161
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Gemma-3-27B, Qwen-3.5-4B and others5
    Abstract作者称 LODO 露出 CV 高估，数据集身份与安全方向部分重叠，标准域泛化干预关不上。

    LODO 用来检查恶意提示词分类器换成没见过的数据集后，还剩多少检测能力。。

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