Transferability of adversarial attacks refers to the phenomenon where adversarial examples generated to attack one machine learning model can also successfully deceive other models, even if those models have different architectures or training datasets. This property of adversarial examples poses significant challenges to model robustness and security, as it implies that attackers can potentially target a wide range of systems with a single crafted input.