How is loss propagated in stacked classifiers? In a 2-stage stacked classifier the first model takes the input data and outputs feature vectors, which are then fed into a second model as input. The second model learns the mapping between the output of the first model and the data labels.
But what mapping does the first model learn? It is given the input data, but how can it learn learn relevant features if it itself is not seeing the labels? There are no "correct" feature vectors that the first model can calculate loss off of.
Is it possible to propagate
the loss from the second model to the first model somehow?

In the common stacked classifier setup, the base learners train using the true labels; they do not receive feedback from the second model.  And generally, they don't produce feature vectors, just a single prediction; it is those predictions from several base learners that produces the feature vector for the second model.
