Security Issues in GPT Engineer Code

No authentication system. No input validation. No CSRF protection. If a database exists, it's directly queryable from the client. API keys may be embedded in frontend code

Add authentication (NextAuth, Supabase Auth, or Clerk). Add a server-side API layer between the frontend and database. Implement input validation. Move all secrets to environment variables. Add CORS and CSRF protection

Every GPT Engineer app needs authentication — verifying who the user is — and authorization — verifying what they're allowed to do. Check that every API route and server action verifies the user's identity before processing requests. Check that users can only access their own data. A common GPT Engineer pattern is adding auth to the UI but not the API, which means anyone with the endpoint URL can access data directly.

Never trust data coming from the client. Every form submission, URL parameter, and API request body should be validated server-side before processing. Use a schema validation library like Zod to define expected shapes and reject anything that doesn't match. This prevents injection attacks, data corruption, and unexpected crashes.

Configure security headers to protect against common web attacks: Content-Security-Policy to prevent XSS, Strict-Transport-Security to enforce HTTPS, X-Frame-Options to prevent clickjacking, and X-Content-Type-Options to prevent MIME sniffing. Most hosting platforms let you configure these in a headers file or configuration.

If your app handles user data, processes payments, or stores sensitive information, a professional security review is essential before launch. Our security scan ($19) checks for the most critical vulnerabilities, and our full security review service provides a comprehensive assessment with remediation guidance.