# Is boosting resistant to overfitting for both number of iterations and number of features?

Boosting methods (such as the popular xgboost) do not tend to overfit when we use many iterations - Schapire and Freund. Are they also resistant to overfitting when we feed them with a large number of features (where some of the features are not very useful?) If so, is there a theoretic connection between these two desirable properties?