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Oct 12 · Monday

All papers

Found 3 papers
Filter4

System type

Models and APIs3

Attacker access

Black-box3White-box5

Attack surface / entry point

User input11Memory writes0Images, audio and video3

Layer

Prompt layer32Model layer3Application layer3

System component

Multimodal3
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.
Attacker access：Black-boxAttack surface / entry point：Memory writesAttack surface / entry point：Images, audio and videoLayer：Model layerClear all

Another 233papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.09703

    研究揭示 Token-Pruning 对 VLM 安全性的影响并提出 SAP 缓解机制

    提出 Safety-Aware Pruning，缓解视觉 token 剪枝放大的多模态越狱

    arXiv
    2610.09703
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    LLaVA-1.5-7B
    Abstract剪枝去掉背景 token 后注意力塌向恶意前景。

    这篇工作评估视觉 token 剪枝如何改变 VLM 在多模态越狱下的行为，并给出推理时防御 SAP。

    Paper analysisRead analysis
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