Statistical wisdom suggests that very complex models, interpolating training data, will be poor at prediction on unseen examples. Yet, this aphorism has been recently challenged by the identification of benign overfitting regimes, specially studied in the case of parametric models: generalization capabilities may be preserved despite model high complexity. While it is widely known that fully-grown decision trees interpolate and, in turn, have bad predictive performances, the same behavior is yet to be analyzed for random forests. We study the trade-off between interpolation and consistency for several types of random forest algorithms.