视觉语言模型的自动提示注入排版攻击方法

胡云峰; 张茗客; 麻斌

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视觉语言模型的自动提示注入排版攻击方法

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- 视觉语言模型的自动提示注入排版攻击方法
- Automated Typographic Prompt Injection Attack on Vision-Language Models
- 西安交通大学学报 2026年
- 作者机构：
  
  1.吉林大学通信工程学院，130022，吉林长春
  2.吉林大学汽车底盘集成与仿生全国重点实验室，130025，吉林长春
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TB27
- 收稿：2026-06-30，
  
  修回：2026-09-06，
  
  录用：2026-09-08，
- 稿件说明：
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胡云峰, 张茗客, 麻斌. 视觉语言模型的自动提示注入排版攻击方法[J]. 西安交通大学学报,2026.

HU Yunfeng, ZHANG Mingke, MA Bin. Automated Typographic Prompt Injection Attack on Vision-Language Models[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026.
胡云峰, 张茗客, 麻斌. 视觉语言模型的自动提示注入排版攻击方法[J]. 西安交通大学学报,2026. DOI：

HU Yunfeng, ZHANG Mingke, MA Bin. Automated Typographic Prompt Injection Attack on Vision-Language Models[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026. DOI：

摘要

针对当前视觉语言模型多模态暴露出的安全脆弱性，本文提出了一套针对自动驾驶场景的自动提示注入排版攻击，构建了一条从驾驶场景感知、自动生成对抗语义、物理自适应渲染到跨模态决策劫持的端到端闭环链路。首先，利用视觉语言模型感知原始驾驶场景状态，通过大语言模型自动生成具有强语义翻转和诱导性的攻击文本指令；其次，结合目标检测模型，将攻击文本自适应地渲染并排版至画面中的合理物理载体上；最后，引入云端大语言模型作为自动化裁判，通过对比攻击前后的感知与规划报告，精准计算导致危险决策的攻击成功率。结果表明：在Qwen-VL、InternVL、BLIP和自动驾驶专精模型DriveMM四种模型上，本文所设计的自动提示注入排版攻击能够成功欺骗包括Qwen等各类先进视觉语言模型，攻击成功率分别为67.55%、61.51%、30.57%、10.38%。

Abstract

In response to the security vulnerabilities exposed by current vision-language models in multimodal scenarios

this study proposes an Automated Typographic Prompt Injection Attack tailored for autonomous driving scenarios. The proposed method constructs an end-to-end closed-loop pipeline consisting of autonomous driving scene perception

automated adversarial semantic generation

physically adaptive rendering

and cross-modal decision hijacking. Specifically

a Vision-Language Model (VLM) is first employed to perceive the original driving scene state

after which a Large Language Model (LLM) automatically generates adversarial text instructions with strong semantic inversion and deceptive effects. Subsequently

combined with an object detection model

the generated attack texts are adaptively rendered and typeset onto appropriate physical carriers within the scene. Finally

a cloud-based Large Language Model is introduced as an automated referee to accurately evaluate the attack success rate of inducing hazardous decisions by comparing the perception and planning reports before and after the attack.Experimental results demonstrate that the proposed Automated Typographic Prompt Injection Attack successfully deceives various advanced Vision-Language Models

including Qwen-VL

InternVL

BLIP

and the autonomous driving specialized model DriveMM

achieving attack success rates of 67.55%

61.51%

30.57%

and 10.38%

respectively.

关键词

Keywords

references

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