Label leakage occurs when future or target information sneaks into input features.

This often happens through timestamp misuse, aggregated features, or improperly joined datasets. The model appears highly accurate but fails in production. Audit features for causal validity and simulate prediction using only information available at inference time.

Common mistakes:

1. Using post-event aggregates

2. Joining tables without time constraints

3. Trusting unusually high validation scores

If performance seems too good, investigate.