  In recent years we have seen substantial advances in foundation models for
artificial intelligence, including language, vision, and multimodal models.
Recent studies have highlighted the potential of using foundation models in
geospatial artificial intelligence, known as GeoAI Foundation Models, for
geographic question answering, remote sensing image understanding, map
generation, and location-based services, among others. However, the development
and application of GeoAI foundation models can pose serious privacy and
security risks, which have not been fully discussed or addressed to date. This
paper introduces the potential privacy and security risks throughout the
lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for
research directions and preventative and control strategies. Through this
vision paper, we hope to draw the attention of researchers and policymakers in
geospatial domains to these privacy and security risks inherent in GeoAI
foundation models and advocate for the development of privacy-preserving and
secure GeoAI foundation models.
