  In recent years, Machine Learning (ML) models have achieved remarkable
success in various domains. However, these models also tend to demonstrate
unsafe behaviors, precluding their deployment in safety-critical systems. To
cope with this issue, ample research focuses on developing methods that
guarantee the safe behaviour of a given ML model. A prominent example is
shielding which incorporates an external component (a "shield") that blocks
unwanted behavior. Despite significant progress, shielding suffers from a main
setback: it is currently geared towards properties encoded solely in
propositional logics (e.g., LTL) and is unsuitable for richer logics. This, in
turn, limits the widespread applicability of shielding in many real-world
systems. In this work, we address this gap, and extend shielding to LTL modulo
theories, by building upon recent advances in reactive synthesis modulo
theories. This allowed us to develop a novel approach for generating shields
conforming to complex safety specifications in these more expressive, logics.
We evaluated our shields and demonstrate their ability to handle rich data with
temporal dynamics. To the best of our knowledge, this is the first approach for
synthesizing shields for such expressivity.
