Skip to content

Translation in progress Some entries still include text in the original language.

Oct 13 · Tuesday

All papers

Found 8 papers
Filter2

Attack type

Jailbreaks8Prompt injection68Poisoning and backdoors77Extraction and theft3Detection evasion45Malicious use3

Impact

Harmful content221Unauthorized actions17Data and privacy leaks5Output manipulation8

System type

Models and APIs7

Attack surface / entry point

User input6

Layer

Prompt layer4Model layer3

System component

Guardrails and judges3Multimodal3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 215papers have no tags yet and are not included in filtered results.
Attack type：JailbreaksImpact：Output manipulationClear all

Another 215papers do not have category tags yet and are not included in filtered results.

  1. Evaluation and benchmarksarXiv 2610.11112

    EpiReal-Bench：商用图像生成模型虚假声明红队基准

    构建 EpiReal-Bench，评测图像生成器将虚假主张渲染为可信证据

    arXiv
    2610.11112
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    GPT-Image-2, Nano Banana 2, Grok Imagen 2.0 and others4
    Abstract四款商用图像模型能把假主张画成可信证据。

    EpiReal-Bench 用来量商用图像生成器会不会把虚假现实主张画成可信视觉证据。

    Paper analysisRead analysis
  2. 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。

    Paper analysisRead analysis
  5. AttacksarXiv 2609.03247

    研究揭示大语言模型的自发身份认证失败：五款模型的分阶段开发者身份实验

    提出 MIPC 与 CFA，观察模型自拟测验后接受开发者身份

    arXiv
    2609.03247
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    ChatGPT, Claude, Qwen3.7-Plus and others5
    Abstract自拟测验可造成对话层虚假认证，但所测授权边界并未因此改变。

    这项工作把“模型自己出题、自己批改、再宣布身份已核验”定义成对话层认证失败，并与后端提权分开。

    Paper analysisRead analysis
  7. 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.

    Paper analysisRead analysis
You have reached the end