Can OpenAI Codex apps be hacked?

OpenAI Codex-Specific Attack Vectors

These are the paths attackers actually take into OpenAI Codex applications — not a generic OWASP list, but what automated scanners and security researchers find when they look at OpenAI Codex apps specifically, given the stack (Supabase (Postgres + RLS) as the database):

1. **Test Credentials in Production**: Codex may generate working code with test API keys that persist to deployment.

2. **Missing Input Validation**: Generated endpoints may accept and process user input without sanitization.

3. **Weak Auth Defaults**: Authentication code may work but lack rate limiting, email verification, or CSRF protection.

4. **Database Access Without Authorization**: Queries may fetch data without checking if the user owns it.

**Supabase-Specific Risk**: OpenAI Codex apps typically ship with the public Supabase anon key embedded in frontend code. That is by design — but only works safely if Row Level Security is enabled on every table. Attackers routinely query Supabase endpoints directly using the anon key from your bundle. A single table without RLS is a full data leak.

How these issues get discovered

This isn't targeted — automated scanners run across the entire internet looking for known patterns, and OpenAI Codex apps surface like everything else. Supabase URLs follow a predictable pattern (`*.supabase.co`), making OpenAI Codex apps easy to fingerprint. Once identified, the scanner probes the specific vulnerability classes listed above.

What a security scan of a OpenAI Codex app looks at

• **Secrets Detection** — Find test credentials and API keys in generated code.
• **Input Validation** — Check all endpoints for proper input sanitization.
• **Auth Security** — Test authentication for rate limiting and session handling.
• **Data Authorization** — Verify users can only access their own data.