  Foundation models--such as GPT, CLIP, and DINO--have achieved revolutionary
progress in the past several years and are commonly believed to be a promising
approach for general-purpose AI. In particular, self-supervised learning is
adopted to pre-train a foundation model using a large amount of unlabeled data.
A pre-trained foundation model is like an ``operating system'' of the AI
ecosystem. Specifically, a foundation model can be used as a feature extractor
for many downstream tasks with little or no labeled training data. Existing
studies on foundation models mainly focused on pre-training a better foundation
model to improve its performance on downstream tasks in non-adversarial
settings, leaving its security and privacy in adversarial settings largely
unexplored. A security or privacy issue of a pre-trained foundation model leads
to a single point of failure for the AI ecosystem. In this book chapter, we
discuss 10 basic security and privacy problems for the pre-trained foundation
models, including six confidentiality problems, three integrity problems, and
one availability problem. For each problem, we discuss potential opportunities
and challenges. We hope our book chapter will inspire future research on the
security and privacy of foundation models.
