Do anomalous input features to autoencoder result in high errors on the corresponding output features?
The output error will be propagated to the subsequent layers, so if your auto-encoder learns a function over all the features, the anomaly will be weaved across all the features you used as input. One trick is to map specific features (or set of features) to different auto-encoders, and train them as an ensemble. Whether you desire to integrate the specific features anomaly in a global outlier score, or maintain outlier scores for subset of features is then a design choice. For a good use case, see this recent paper.