On the Adversarial Robustness of Multi-Modal Foundation Models
Christian Schlarmann Matthias Hein
Multi-modal foundation models combining vision and language models such as
Flamingo or GPT-4 have recently gained enormous interest. Alignment of
foundation models is used to prevent models from providing toxic or harmful
output. While malicious users have successfully tried to jailbreak foundation
models, an equally important question is if honest users could be harmed by
malicious third-party content. In this paper we show that imperceivable attacks
on images in order to change the caption output of a multi-modal foundation
model can be used by malicious content providers to harm honest users e.g. by
guiding them to malicious websites or broadcast fake information. This
indicates that countermeasures to adversarial attacks should be used by any
deployed multi-modal foundation model.