利用病毒感染生成式人工智能

摘要

本研究展示了一种新颖的方法，通过在JPEG图像中嵌入EICAR测试文件来测试Vision-Large Language Model（VLM/LLM）的安全边界。我们成功地在多个LLM平台上执行了四种不同的协议，包括OpenAI GPT-4o、Microsoft Copilot、Google Gemini 1.5 Pro和Anthropic Claude 3.5 Sonnet。实验证实，包含EICAR签名的修改后的JPEG文件可以被上传、操作，并可能在LLM虚拟工作空间内执行。关键发现包括：1）能够在图像元数据中掩盖EICAR字符串而不被检测到，2）成功使用基于Python的操作在LLM环境内提取测试文件，3）展示了多种混淆技术，包括base64编码和字符串反转。本研究将微软研究的“渗透测试规则”框架扩展到评估基于云的生成式人工智能和LLM的安全边界，特别关注容器化环境内的文件处理和执行能力。

English

This study demonstrates a novel approach to testing the security boundaries of Vision-Large Language Model (VLM/ LLM) using the EICAR test file embedded within JPEG images. We successfully executed four distinct protocols across multiple LLM platforms, including OpenAI GPT-4o, Microsoft Copilot, Google Gemini 1.5 Pro, and Anthropic Claude 3.5 Sonnet. The experiments validated that a modified JPEG containing the EICAR signature could be uploaded, manipulated, and potentially executed within LLM virtual workspaces. Key findings include: 1) consistent ability to mask the EICAR string in image metadata without detection, 2) successful extraction of the test file using Python-based manipulation within LLM environments, and 3) demonstration of multiple obfuscation techniques including base64 encoding and string reversal. This research extends Microsoft Research's "Penetration Testing Rules of Engagement" framework to evaluate cloud-based generative AI and LLM security boundaries, particularly focusing on file handling and execution capabilities within containerized environments.

利用病毒感染生成式人工智能

Infecting Generative AI With Viruses

摘要

Summary

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