My AI-built app works. Is it safe to ship?
Working AI-generated code still needs a security review. Begin with the places where untrusted input reaches data, money or privileged actions. Scan the repository, verify each finding, and test those boundaries with different users before deploying.
Who this is for: Founders and developers building with Cursor, Claude Code, ChatGPT, Lovable, v0 or GitHub Copilot.
STEP 01
Review the change, then follow the data
A generated feature can compile and pass a happy-path test while allowing one customer to read another customer's records. Start with the changed endpoints, server actions and database queries. Trace the user identity and resource owner through each operation. Do not assume a hidden button or a protected page also protects its backend endpoint.
Try this: For each new endpoint, record who may call it, which records they may touch, and where that permission is checked.
STEP 02
Give the scanner enough context
A pasted function is useful for a focused check, but repository context can reveal imports, configuration and dependency manifests that a snippet omits. Include the relevant source and lockfiles while keeping unnecessary customer data out of the scan. Review generated SQL migrations and deployment configuration alongside application code.
Try this: Use a snippet scan during development and a repository review before release. Record the commit that was examined.
STEP 03
Make the assistant's fix earn its place
An assistant can propose a plausible patch without understanding your permissions model. Review the diff, test both an allowed action and a denied action, and run the relevant application tests. Re-scanning checks for patterns the scanner recognizes; the regression test demonstrates that your intended boundary now holds.
Try this: Turn every confirmed authorization issue into a test using an unauthorized or differently scoped account.
Your review checklist
- Review every new server endpoint and its authorization check.
- Check generated SQL policies and tenant ownership filters.
- Search source and build output for exposed secrets.
- Verify suggested packages exist and inspect installed versions.
- Validate payment events on the server before granting access.
- Review the diff, run regression tests and scan the release commit.
What Bryxe can check
Bryxe combines supported static code checks, secret detection and dependency checks. Findings give developers a starting point for investigation and remediation; the public AI code audit tool provides a focused entry point.
What you still need to verify
No scanner can establish that all generated code is safe. Runtime configuration, tenant permissions and business rules still require testing in your application. Tool coverage varies by language and scan mode.
Common questions
Is code from Cursor or Claude Code automatically insecure?
No. The tool used to write code does not determine its security. Evaluate the resulting implementation, dependencies, permissions and deployment configuration. Apply the same review standard to generated and human-written changes.
What should I paste into an AI code security checker?
Use the relevant function together with its input validation and authorization logic. Replace credentials and personal data with placeholders. If the issue depends on imports or configuration, review the repository rather than relying on a standalone snippet.
Can Bryxe fix every finding automatically?
Do not assume every finding has a safe automatic fix. Review any proposed patch, test its behavior and re-scan. Feature availability depends on the scan mode and plan; the pricing page describes the current options.
Documentation & further reading
These primary sources explain the platform behavior discussed above. Check the documentation for the versions and configuration you use.