domains / families / security / detecting-data-and-model-poisoning

detecting-data-and-model-poisoning

separate

Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

When to use

• Before training/fine-tuning on third-party or user-contributed data.
• Before deploying a model built on a downloaded pre-trained checkpoint.
• During an ML supply-chain security review.
• When investigating anomalous model behavior tied to specific inputs (possible backdoor trigger).
• As a CI/CD gate that scans datasets and model artifacts before they enter the pipeline.

When not to use

• Deploying backdoored models
• Distributing poisoned datasets

Skill page

Source