Codex
Security FAQ

What vulnerabilities are found in OpenAI Codex apps?

Get instant answers about your app's security.

Short Answer

OpenAI Codex app scans surface the same cluster of vulnerabilities repeatedly: test credentials in production, missing input validation, weak auth defaults. The pattern is stable across OpenAI Codex versions.

Detailed Answer

The vulnerabilities actually found in OpenAI Codex apps

Not theoretical OWASP categories — specifically what appears when VAS, security researchers, and bug bounty hunters look at live OpenAI Codex deployments:

  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.

Distribution by severity

Of the findings above, 0 sit at critical impact (full data exposure), 0 at high (significant data or account compromise), and the rest are medium-or-lower (attack surface expansion). A first-scan OpenAI Codex app typically has 2–4 findings from this list live at any moment.

How to know which ones are in your app

Run a VAS scan. Each finding above is tested directly — we query your database to verify access controls are active, scan bundles for key patterns, probe auth endpoints for rate limiting, and check security headers in live responses. Output is a per-finding report with evidence and fix.

Security Research & Statistics

10.3%

of Lovable applications (170 out of 1,645) had exposed user data in the CVE-2025-48757 incident

Source: CVE-2025-48757 security advisory

4.45 million USD

average cost of a data breach in 2023

Source: IBM Cost of a Data Breach Report 2023

500,000+

developers using vibe coding platforms like Lovable, Bolt, and Replit

Source: Combined platform statistics 2024-2025

Expert Perspectives

There's a new kind of coding I call 'vibe coding', where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.

Andrej KarpathyFormer Tesla AI Director, OpenAI Co-founder

Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial.

Simon WillisonSecurity Researcher, Django Co-creator

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More Questions About This Topic

How severe are the vulnerabilities typically found in OpenAI Codex apps?

OpenAI Codex apps lean critical: Test Credentials in Production alone can expose the full user dataset in one query. Compare to e.g. missing security headers (medium) which require additional exploitation. Triage accordingly: critical findings are measured in minutes-to-breach, mediums in weeks.

How do I fix vulnerabilities once they're found in my OpenAI Codex app?

Start with critical impact findings, apply the remediation guidance per finding, and re-scan. Never "fix and hope" — confirm with a second scan. Many fixes (e.g., enabling RLS) are one-line; others (e.g., moving a secret server-side) require structural changes to where the value is used.

Can vulnerabilities in OpenAI Codex apps be exploited by a non-expert attacker?

Most can. Extracting an exposed API key is a single "view source" operation. Querying a table without RLS is a `curl` command. Exploiting missing rate limiting requires scripting skills equivalent to "follow a tutorial." Only a handful of the findings above (e.g., chained auth bypass) require specialist knowledge — the rest are routinely exploited by automated scanners with zero human involvement.