Infecting Generative AI With Viruses
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.
6784b162057f36b0bf64cd28
David Noever
admin_assigned
2025-01-13T09:34:39.568Z
63136a82e29fb2e86d5e5bdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
David Noever
user
dnoever
6784b162057f36b0bf64cd29
Forrest McKee
admin_assigned
2025-01-13T09:34:45.211Z
627bff703974b0ed6b2c340d
/avatars/12518ccef145f343548b3fd7be5efddc.svg
Forrest McKee
user
fgmckee
Infecting Generative AI With Viruses
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.
Research evaluates LLM security boundaries by embedding executable EICAR test files in JPEG images across multiple platforms, demonstrating consistent obfuscation and extraction capabilities.
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 image metadata Python-based manipulation base64 encoding string reversal Penetration Testing Rules of Engagement cloud-based generative AI