ml-security
Secure ML systems and meet compliance requirements. Covers adversarial attacks and robustness testing, data poisoning detection, model extraction and inversion defense, differential privacy (Opacus, TensorFlow Privacy), federated learning security, model encryption, signing and verification, supply chain security for ML artifacts, input validation for ML APIs, audit logging, PII detection, data anonymization, OWASP ML Top 10, MITRE ATLAS, secret management, secure serving with TLS and authentication, rate limiting, dependency scanning, and fairness auditing. Use when securing models or ML pipelines, scanning artifacts, detecting or anonymizing PII, threat-modeling an ML system, or handling compliance and privacy requirements such as GDPR, CCPA, HIPAA, DPIA, data residency, or audit obligations.
- Version
- 1.0
- License
- Apache-2.0
Pinned to revision 45cf0fa3c5e7, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/ml-security/SKILL.md
- skills/ml-security/references/REFERENCE.md
- skills/ml-security/scripts/privacy_guard.py
- skills/ml-security/scripts/security_scan.py
Every link opens the file at its source, pinned to the revision this page describes.