01Introduction
AI coding assistants now write a large share of new code, and vibe coding lets people ship whole apps without reading them. The code usually works. Whether it is safe is a separate question that nobody asked the assistant.
02What is AI-generated code security?
AI-generated code security is the practice of finding and preventing vulnerabilities in code produced by assistants such as GitHub Copilot, Cursor, Claude Code and similar tools before it reaches production.
It is a volume and trust problem for application security: more code, written faster, often reviewed less. AI code review is the natural counterweight, and SCA matters because assistants suggest packages that are outdated or do not exist.
03How AI-generated code security works
Teams that ship AI-written code safely do a few things consistently.
- 1.
Review every change
Treat assistant output like a pull request from a new hire, with automated security review on every diff.
- 2.
Verify dependencies
Check that suggested packages exist, are maintained and are not typosquats.
- 3.
Guard secrets
Stop assistants from pasting keys into code or config. See secrets detection.
- 4.
Test the running app
Validate behavior with DAST or pentesting, because assistants copy insecure patterns that look fine in isolation.
04Threats and risks
Assistant-written code has recognizable failure modes.
Missing authorization
Endpoints that work perfectly and check nothing. See broken access control.
Insecure defaults
Disabled TLS verification, permissive CORS and debug settings copied from training data.
Hallucinated packages
Imports of packages that do not exist, which attackers then register.
Rubber-stamp reviews
Large generated diffs get approved because they pass tests.
05How Parameter helps
Parameter reviews and tests AI-written code at the speed it arrives.
Every PR reviewed
Sentinel reviews more than 10,000 pull requests a day, whether a person or an assistant wrote them.
Dependencies checked
Supply Chain flags risky and malicious packages with reachability context.
Behavior proven
AI Pentesting exercises the deployed app, catching flaws that only show up at runtime.
