The Silent Threat: Securing Machine Learning Models in the 2030s In the relentless pursuit of ever-more-capable machine learning models, a critical vulnerability often lurks beneath the surface: susceptibility to adversarial attacks. These subtle, often imperceptible, perturbations to input data can cause even the most sophisticated models to falter, leading to misclassifications and potentially catastrophic consequences.
