Feature leakage is one of the most dangerous failures in machine learning because your model looks excellent on paper. Accuracy jumps, losses drop, cross-validation smiles at you… and yet the model is learning information it should never have access to.
Leakage hides in subtle places; columns updated after an event happens, IDs that encode outcome patterns, or features computed using future timestamps. Nothing looks suspicious, but the model is essentially borrowing tomorrow’s truth to predict today.
The only real defense is time awareness. Before allowing any feature into training, ask:
Would this value truly exist at the moment of prediction?
If the answer is no, the model isn’t learning. It’s cheating.