Does class weighting encourage overfitting when the true class distribution is imbalanced?

I am working on a classification problem in which ~90% of samples come from class 1 while ~10% of samples come from class 2. I have been using various techniques to combat the class imbalance while learning the problem, however, I am concerned about potential bias this may introduce because the true class distribution is unknown. Is it bad practice to weight classes during learning if the true distribution (or some reasonable approximation) is unknown?

Your assessment is right. You must first determine the data distribution in real-time (production) and only after that proceed with train_set, test_set and validation_set creation with the same distribution. And subsequently work on model training and setting the class weightages if required.