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Oct 12 · Monday

All papers

Found 6 papers
Filter3

Attack type

Jailbreaks44Prompt injection6Poisoning and backdoors5Extraction and theft4Detection evasion23Malicious use6

Attack technique

Gradient and representation optimization4Cross-modal payloads4

Impact

Output manipulation6

System type

Models and APIs6AI Agent11web agent4Browser and computer use3

Attack surface / entry point

User input9Tool responses4Images, audio and video6

Layer

Prompt layer5

System component

Vision5Multimodal6
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 265papers have no tags yet and are not included in filtered results.
Attack type：Prompt injectionSystem type：Models and APIsAttack surface / entry point：Images, audio and videoClear all

Another 265papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.09027

    P-VMI：对抗图像经 KV cache 持续影响多轮对话

    提出持久视觉记忆注入，使对抗图像经缓存在触发时操控输出

    arXiv
    2610.09027
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-8B, Qwen3-VL-8B-Instruct, Gemma-4-12B

    Abstract论文提出 P-VMI，证实对抗输入被掩蔽后仍可经 KV 缓存持久控制模型，强调了保留状态的安全风险。

    Paper analysisRead analysis
  2. Evaluation and benchmarksarXiv 2609.04859

    MM-IFEval-Pro：面向视觉语言模型的多语言抗攻击指令遵循基准

    提出抗攻击多语言视觉指令遵循基准 MM-IFEval-Pro

    arXiv
    2609.04859
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-VL-8B-Instruct, InternVL3.5-8B, MiniCPM-V4.5 and others5
    AbstractMM-IFEval-Pro 以规则验证评测中英指令遵循与视觉劫持，GRPO 提高两基座平均分。

    MM-IFEval-Pro 是面向 VLM 的中英多模态指令遵循基准，并配有可用规则计算奖励的 GRPO 训练集。

    Paper analysisRead analysis
  4. AttacksarXiv 2604.14604

    AudioHijack 框架对 13 个语音大模型实现隐蔽音频提示注入

    提出 AudioHijack，用不可感知对抗音频对语音模型做上下文无关提示注入

    arXiv
    2604.14604
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    SpeechGPT, GLM-4-Voice, VITA-Audio and others15

    Abstract提出了 AudioHijack 框架，揭示了 LALM 对听觉提示注入的脆弱性与传统防御的局限。

    Paper analysisRead analysis
  6. AttacksarXiv 2602.15927

    研究者提出面向多轮对话的视觉记忆注入攻击 VMI

    提出 VMI，用扰动图像在多轮对话中潜伏并仅在触发话题输出预定消息

    arXiv
    2602.15927
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2.5-VL-7B-Instruct, Qwen3-VL-8B-Instruct, LLaVA-OneVision-1.5-8B-Instruct and others5
    AbstractVMI 让扰动图像在多轮 LVLM 中潜伏，作者称触发话题出现后仍可输出预定消息。

    VMI 是面向多轮 LVLM 的定向图像攻击：第三方发布带小扰动的图像，良性用户把它当作普通输入。

    Paper analysisRead analysis
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