API - Sandbox

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API - Sandbox

A "crippled" API key (also called a restricted, limited, sandbox, or scoped key) is a deliberately constrained version of a full API key. It's designed for safe sharing with external users, evaluators, beta testers, or third parties during model evaluation—without risking your main account, high costs, or full access.

Why Use a Crippled Key for Model Evaluation?

  • Prevents abuse (e.g., users generating massive outputs or attacking the system).
  • Limits financial exposure (usage caps or quotas).
  • Restricts scope (specific models, endpoints, or features only).
  • Enables easy revocation or expiration.
  • Reduces security risks if the key leaks.

This is common in LLM platforms (e.g., OpenAI, Anthropic, Google, xAI/Grok, or custom gateways) for testing new models safely.

Common Ways to "Cripple" an API Key

Here are practical approaches, from simplest to more advanced:

  1. Short Expiration Date Set the key to auto-expire after hours, days, or a week. Ideal for one-off evaluations. Many dashboards let you pick an expiry when creating the key.
  2. Strict Rate Limits & Quotas
    • Low requests per minute (RPM) or tokens per minute (TPM), e.g., 10–50 RPM and small context limits.
    • Hard spending cap (e.g., $5–$20 total usage).
    • Daily/monthly usage limits. This stops runaway costs or flooding during evaluation.
  3. Model Scoping (Allowed Models Only) Restrict the key to specific models (e.g., only your new evaluation model, not production ones). Requests to other models return 403 Forbidden. Example config: allowed_models: ["your-eval-model-v1"].
  4. Permission/Endpoint Restrictions
    • Read-only or inference-only (no fine-tuning, no admin actions).
    • Block certain features (e.g., no tool calling, no image generation if evaluating text-only).
    • Limit to specific API endpoints.
  5. IP / Referrer / Application Restrictions Whitelist specific IPs, domains, or app package names so the key only works from your evaluation environment or users' controlled setups.
  6. Sandbox / Isolated Environment Point the key at a sandbox deployment (separate instance with dummy data, lower resources, or mock responses for non-critical tests). Some platforms provide dedicated sandbox keys.
  7. Usage Monitoring + Auto-Revocation Log all activity tied to the key. Set alerts for anomalies and revoke instantly if needed.

How to Implement This in Practice

  • Provider Dashboards (OpenAI, xAI, Anthropic, etc.): Most allow creating keys with name, expiration, model restrictions, and permissions directly in the console.
  • API Gateway / Proxy Layer (recommended for advanced control):
    • Use tools like an LLM gateway (e.g., custom with Kong, Tyk, or services like TrueFoundry, ngrok AI Gateway).
    • The gateway accepts your crippled key, enforces extra rules (rate limits, content filters, PII redaction), and forwards to the backend with a full internal key.
    • This adds a safety layer even if the provider's native restrictions are limited.
  • Custom Backend: If you're hosting your own model evaluation service, implement key validation in your code with libraries for RBAC, rate limiting (e.g., Redis-based), and quotas.

Example Creation Flow (Generic)

  1. Go to your API console → API Keys section.
  2. Create new key → Name it clearly (e.g., "Model-Eval-UserX-2026").
  3. Set:
    • Expiration: 7 days
    • Allowed models: Only the evaluation model
    • Rate limits: Low RPM/TPM + spending cap
    • Permissions: Inference only
  4. Generate → Copy the key once (it usually won't show again).
  5. Share it with the user (via secure channel, never email plaintext if possible).
  6. Monitor usage in the dashboard and revoke when evaluation ends.

Security Tips When Sharing

  • Never give full/unrestricted keys to external users.
  • Use unique keys per evaluator so you can revoke individually.
  • Combine with user authentication if possible (e.g., OAuth flow instead of raw keys for better control).
  • Store your master keys securely (secrets manager, never in code).
  • Test the crippled key thoroughly before distributing.

If you're building this for a custom LLM service or specific provider (e.g., xAI/Grok API, OpenAI, etc.), the exact UI/options vary—let me know which platform you're using for more tailored steps.

This setup keeps evaluations productive while protecting your resources and costs.

 

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