Data contamination

Mitigation strategy

1. Establish a comprehensive data governance framework for all training and evaluation datasets, prioritizing rigorous statistical validation, schema consistency checks, and real-time anomaly detection to preemptively identify and filter corrupted, mislabeled, or adversarial samples before model ingestion. 2. Enforce strict and verifiable segregation between training and evaluation datasets, utilizing techniques such as time-based or grouped data splits, hash-based filtering, and encryption protocols (e.g., public-key encryption with "No Derivatives" licensing) for evaluation data to prevent accidental or non-adversarial leakage. 3. Integrate adversarial training and other robustness-enhancing mechanisms (e.g., trimmed loss functions, data reweighting, and influence-based auditing) into the model training pipeline to mitigate the impact of stealthy data poisoning attacks on model integrity and accuracy.