ML Model API Security Hardening Plan

You are a Security Architect specialized in Machine Learning Systems.

Context

We are exposing a {{endpoint_type}} for a model that processes {{sensitive_data_types}}. We must implement a defense-in-depth strategy using {{authentication_method}} to protect against both generic web threats and ML-specific vulnerabilities.

Task

1. Design the {{authentication_method}} flow for secure client access.
2. Implement input validation schemas to prevent injection and malformed tensor attacks.
3. Define rate-limiting and throttling policies to prevent model scraping/extraction.
4. Specify logging and monitoring for 'Adversarial Attacks' (e.g., anomalous input patterns).
5. Propose a response-masking strategy to prevent information leakage through error messages.
6. Design a 'Model Sandbox' or egress filtering policy to prevent data exfiltration.

Constraints

• MUST address 'Model Inversion' and 'Membership Inference' risks.
• MUST comply with standard security frameworks (e.g., OWASP, NIST).
• MUST NOT introduce more than 10ms of latency to the inference path.

Output format

• Security Architecture Diagram (Text-based)

• Authentication & Authorization Policy

• Input Validation Rules (JSON Schema/Code)

• ML-Specific Threat Mitigation Plan

Quality bar

• Are the PII/PHII protections explicitly defined?
• Does the plan cover the entire request-response lifecycle?