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Oct 13 · Tuesday

All papers

Found 9 papers
Filter4

Attack type

Jailbreaks9Prompt injection8Detection evasion5

Attack technique

Gradient and representation optimization4Automated search8Multi-turn escalation5Encoding and obfuscation12Cross-modal payloads27

Impact

Harmful content9

System type

Models and APIs9

Attacker access

Black-box6

Attack surface / entry point

User input7Images, audio and video9

Layer

Prompt layer9

System component

Guardrails and judges6Vision9Multimodal11
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Attack type：JailbreaksAttack technique：Gradient and representation optimizationAttack technique：Multi-turn escalationSystem component：VisionClear all

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

  2. AttacksarXiv 2605.07399

    研究者提出 GPO-V，用全局概率优化越狱扩散视觉语言模型

    提出 GPO-V，用全局概率优化的视觉扰动越狱扩散视觉语言模型

    arXiv
    2605.07399
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    White-box, Black-box
    Evaluated models
    LLaDA-V, LaViDA
    Abstract作者用视觉扰动改 dVLM 的全局生成概率，以绕过两类拒绝。

    GPO-V 把越狱从“开头必须顺从”改成“整段去噪概率朝肯定方向走”。。

    Paper analysisRead analysis
  5. AttacksarXiv 2603.20198

    研究者提出 MM-Plan 多模态越狱框架

    提出 MM-Plan 多模态越狱框架，自动规划多轮图像编辑以越狱视觉独占目标

    arXiv
    2603.20198
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    Llama-3.2-11B, InternVL3-8B, Qwen3-VL-8B and others8
    Abstract作者称全局规划能攻破对单轮攻击更稳的前沿 MLLM。

    MM-Plan 是面向 Visual Exclusivity 的 black-box 多模态红队方法，并配有人工基准 VE-Safety。

    Paper analysisRead analysis
  8. AttacksarXiv 2511.16110

    MFA 框架在 17 个视觉语言模型上取得 58.5% 攻击成功率

    提出 MFA 框架，协同突破视觉语言模型的对齐、提示与审核层

    arXiv
    2511.16110
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    MiniGPT-4, LLaMA-4-Scout-Inst, LLaMA-3.2-11B-Vision-Instruct and others17

    Abstract作者称多层防御的 VLM 仍存在跨模型漏洞，MFA 整合三项技术实现了端到端越狱。

    Paper analysisRead analysis
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