Exploiting Alpha Transparency In Language And Vision-Based AI Systems
David Noever Forrest McKee
This investigation reveals a novel exploit derived from PNG image file
formats, specifically their alpha transparency layer, and its potential to fool
multiple AI vision systems. Our method uses this alpha layer as a clandestine
channel invisible to human observers but fully actionable by AI image
processors. The scope tested for the vulnerability spans representative vision
systems from Apple, Microsoft, Google, Salesforce, Nvidia, and Facebook,
highlighting the attack's potential breadth. This vulnerability challenges the
security protocols of existing and fielded vision systems, from medical imaging
to autonomous driving technologies. Our experiments demonstrate that the
affected systems, which rely on convolutional neural networks or the latest
multimodal language models, cannot quickly mitigate these vulnerabilities
through simple patches or updates. Instead, they require retraining and
architectural changes, indicating a persistent hole in multimodal technologies
without some future adversarial hardening against such vision-language
exploits.