ML Systems Threats: Applying the OWASP Machine Learning Security Top 10

The OWASP ML Security Top 10 highlights attacks like input manipulation, data poisoning, model theft and insecure supply chains.

What good looks like

• Dataset integrity: provenance checks, hash manifests, and differential privacy for sensitive features.
• Model artifact security: signed models, SBOMs for training pipelines, and private registries.
• Robustness testing: adversarial evals, drift detection, and red-team scenarios before promotion.
• Runtime protections: rate limits, anomaly detection, and canary models to spot abuse.

Start with critical paths: dataset intake, training, packaging, deployment, and monitoring.