How does SVM behave under curse of dimensionality?

SVM is relatively robust to high dimensions but still suffers from sparsity and noise issues.

Unlike distance-based models, SVM relies on margin maximization, which remains effective in high-dimensional spaces. However, if irrelevant features dominate, performance can degrade without feature selection or regularization.