title: Infecting Generative AI With Viruses
summary: 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.
paper_authors._id: 6784b162057f36b0bf64cd28
paper_authors.name: David Noever
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-13T09:34:39.568Z
paper_authors._id: 63136a82e29fb2e86d5e5bdd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
paper_authors.fullname: David Noever
paper_authors.type: user
paper_authors.user: dnoever
paper_authors._id: 6784b162057f36b0bf64cd29
paper_authors.name: Forrest McKee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-13T09:34:45.211Z
paper_authors._id: 627bff703974b0ed6b2c340d
paper_authors.avatarUrl: /avatars/12518ccef145f343548b3fd7be5efddc.svg
paper_authors.fullname: Forrest McKee
paper_authors.type: user
paper_authors.user: fgmckee
paper_title: Infecting Generative AI With Viruses
paper_summary: 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.
paper_ai_keywords: Vision-Large Language Model (VLM/LLM) EICAR test file JPEG images OpenAI GPT-4o Microsoft Copilot Google Gemini 1.5 Pro Anthropic Claude 3.5 Sonnet EICAR signature image metadata Python-based manipulation base64 encoding string reversal cloud-based generative AI file handling execution capabilities containerized environments