  In the rapidly evolving landscape of artificial intelligence, multimodal
learning systems (MMLS) have gained traction for their ability to process and
integrate information from diverse modality inputs. Their expanding use in
vital sectors such as healthcare has made safety assurance a critical concern.
However, the absence of systematic research into their safety is a significant
barrier to progress in this field. To bridge the gap, we present the first
taxonomy that systematically categorizes and assesses MMLS safety. This
taxonomy is structured around four fundamental pillars that are critical to
ensuring the safety of MMLS: robustness, alignment, monitoring, and
controllability. Leveraging this taxonomy, we review existing methodologies,
benchmarks, and the current state of research, while also pinpointing the
principal limitations and gaps in knowledge. Finally, we discuss unique
challenges in MMLS safety. In illuminating these challenges, we aim to pave the
way for future research, proposing potential directions that could lead to
significant advancements in the safety protocols of MMLS.
