  The analysis of vision-based deep neural networks (DNNs) is highly desirable
but it is very challenging due to the difficulty of expressing formal
specifications for vision tasks and the lack of efficient verification
procedures. In this paper, we propose to leverage emerging multimodal,
vision-language, foundation models (VLMs) as a lens through which we can reason
about vision models. VLMs have been trained on a large body of images
accompanied by their textual description, and are thus implicitly aware of
high-level, human-understandable concepts describing the images. We describe a
logical specification language $\texttt{Con}_{\texttt{spec}}$ designed to
facilitate writing specifications in terms of these concepts. To define and
formally check $\texttt{Con}_{\texttt{spec}}$ specifications, we build a map
between the internal representations of a given vision model and a VLM, leading
to an efficient verification procedure of natural-language properties for
vision models. We demonstrate our techniques on a ResNet-based classifier
trained on the RIVAL-10 dataset using CLIP as the multimodal model.
