Risks of AI-generated code: a checklist before deployment

Security8 min read

A model generates code that looks credible and often works. That is exactly why its flaws are easy to overlook.

Security

Generated code can skip input validation, build database queries in a way that is vulnerable to injection, store passwords without proper hashing or grant overly broad permissions. The model learned from a huge amount of code, including poor-quality code, and does not always tell a tutorial example from a production solution.

Secrets in the repository

API keys and passwords pasted into a file "just for a moment" can end up in the repository history. Keep them in environment variables, add configuration files to the ignore list and check the history before you publish a project.

Dependencies that do not exist

Models can invent package names. If someone registers such a name and puts malicious code in it, installing the "suggested" package becomes an attack vector. Before adding an unfamiliar library, check who maintains it, how long it has existed and how many people use it.

Data privacy

When you paste code snippets, logs or customer data into a tool, you are sending them to an external provider. Check its data processing rules and do not paste anything you are not allowed to share outside.

Licenses

A generated snippet may resemble code covered by a specific license. The legal questions around AI-created code are still being settled and differ between jurisdictions, so for commercial projects it is worth consulting a lawyer.

Technical debt

The costliest side effect is code that nobody on the team understands. Six months from now, when it has to be changed, it will turn out that nobody knows why it does what it does.

Pre-deployment checklist

This list is not a substitute for a security audit. If your application processes payments or sensitive data, you need a review carried out by a specialist.