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Practical baseline safeguards for ChatGPT-powered services

Practical baseline safeguards for ChatGPT-powered services

When building an application using the OpenAI API, it is tempting to ship as soon as “it works.”
However, insufficient safeguards often lead to API key leakage, unexpected traffic spikes, and ultimately serious billing incidents.

This article summarizes practical, lightweight protections that are especially relevant for:

  • personal projects
  • internal tools
  • early-stage prototypes

These are not universal requirements.
What counts as a “baseline” depends heavily on your threat model, user anonymity, billing model, and scale.

  1. A common but underestimated risk

One frequently overlooked risk is direct access to your backend API endpoints, bypassing the UI entirely.

In practice, incidents may also be caused by:

  • abuse by authenticated users
  • bugs or retry loops
  • misconfigured proxies or shared environments

However, direct scripted access to your backend is a very common failure mode, especially when no safeguards exist.

If an attacker can replay the exact same request your frontend sends, they can often exhaust your usage limits in minutes.

This article therefore focuses primarily on protecting your backend (Workers / server) rather than the UI.

  1. Never call the OpenAI API directly from the client

Your frontend should never call the OpenAI API directly.

  • API keys embedded in browsers or apps will leak
  • DevTools, network inspection, and modified requests make this trivial

Recommended architecture

Client → Your backend (Workers / server)
Backend → OpenAI API

The client receives only the processed result.

This allows you to:

  • keep API keys secret
  • validate and throttle requests
  • support streaming safely

OpenAI explicitly recommends this approach in its official documentation.

  1. Always implement rate limiting

Rate limiting is essential, even for small projects.

Typical limits include:

  • per 5 minutes
  • per hour
  • per day

Implementation can be simple:

  • store a counter
  • store the start time of the window

Cloudflare Workers with KV, Durable Objects, or D1 make this straightforward using IPs, user IDs, or session identifiers.

  1. Lightweight one-time tokens (with clear limitations)

To discourage trivial scripted abuse, you may introduce short-lived request tokens.

For example:

  • embed an encrypted timestamp
  • validate that it was generated recently (e.g., within 10 seconds)

⚠️ Important limitations

  • This is not cryptographically strong authentication
  • This does not fully prevent replay within the valid window
  • This must never replace proper authentication

Think of this as friction, not security.

It is useful against:

  • naive replay scripts
  • casual scraping
  • low-effort abuse

It is not sufficient against a determined attacker.

  1. Review auto-recharge and billing assumptions carefully

Billing incidents are often underestimated.

There are real-world reports of:

  • usage continuing briefly after limits were reached
  • negative balances appearing despite prepaid setups

Budget limits should be treated as operational safeguards, not hard guarantees.

For small projects, consider:

  • disabling auto-recharge
  • keeping balances low
  • adding monitoring and alerts

Using a dedicated debit card with a low limit can further cap worst-case damage.

  1. Production services require real authentication

The measures above are suitable for:

  • prototypes
  • internal tools
  • anonymous experiments

For real services, you will need:

  • OAuth or equivalent authentication
  • session and token management
  • per-user quotas and permissions

IP-based checks alone are insufficient, as client-side values can be forged.

Defense must be layered: authentication, rate limits, quotas, and monitoring.

  1. Never let the client choose the model

Model selection directly affects cost.

Best practice:

  • fix allowed models on the server
  • reject anything outside a strict allowlist

Do not rely on obscurity or “confusion” tactics.
Clear validation and rejection are safer, simpler, and easier to operate.

Conclusion

For small-scale ChatGPT-powered services, the following baseline practices already eliminate many real-world incidents:

  • keep API keys on the server
  • enforce rate limits
  • treat billing controls realistically

These measures will not make your system unbreakable — but they raise the cost of abuse enough that most attackers move on to easier targets.

Security is not about perfect defenses, but about making misuse inconvenient and uneconomical.

Original Japanese article (source of revision): https://qiita.com/uni928/items/061432019b316e418902

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