infrastructure-security_skill

Overview

This skill helps secure AI/ML infrastructure by hardening model storage, API endpoints, networking, and compute resources. It provides practical controls, testing routines, and a checklist to reduce risks like model theft, authentication bypass, and resource exhaustion. The focus is defense-in-depth with repeatable configurations and testable validations.

How this skill works

The skill inspects and enforces layered protections across API, model, network, and compute surfaces. It validates authentication, rate limiting, input schemas, storage encryption, container isolation, and TLS settings, and includes vulnerability tests to simulate auth bypass, rate-limit abuse, and input validation failures. Results map to severity levels and actionable remediations.

When to use it

• Before deploying models to production to validate security posture
• During CI/CD pipelines as a gate for infrastructure and API configuration
• When responding to suspected model extraction or unauthorized access
• To audit and harden multi-tenant or externally exposed inference endpoints
• As part of periodic security reviews or red-team exercises

Best practices

• Enforce strong auth: OAuth2, OIDC, API key rotation, and mTLS between services
• Apply token-aware rate limiting and cost-based throttling to prevent abuse
• Limit input length and validate schemas; sanitize error messages
• Encrypt models at rest (AES-256-GCM), enable RBAC, and maintain encrypted backups
• Run containers as non-root with minimal capabilities, read-only filesystems, and ephemeral disks
• Use TLS 1.3, certificate rotation, VPC isolation, and DDoS protection

Example use cases

• Automated pre-deployment check that verifies TLS, auth, and rate limits for a new model endpoint
• Incident response playbook: detect model extraction, enforce stricter query limits and enable output perturbation
• CI/CD security gate that fails builds when container images run as root or secrets are in environment variables
• Periodic red-team test that attempts auth bypass and reports findings mapped to severity levels
• Integration with SIEM to forward comprehensive audit logs from model queries and access events