How does SVM behave under feature redundancy?

SVM remains stable but may become less efficient with redundant features.

Redundant features increase dimensionality without adding new information. Linear SVM can assign near-zero weights to redundant features, but kernel SVM may suffer from increased computation and noise sensitivity.