AI Security Best Practices for Coding Agents: What to Do Differently

By Jai Mittal, CTO · August 16, 2026

AI coding agents — Lovable, Bolt, Cursor, Claude Code, GitHub Copilot, Windsurf — generate production code faster than any human can write it. The security practices that assume a human is writing and reviewing every line of code need to be updated for this reality.

Here are the eight practices that address the specific risks AI coding agents introduce, with concrete implementation steps for each.

1. Run secret scanning before every push

AI agents hardcode credentials during generation. They write the shortest path to working code, and the shortest path to calling the Stripe API is to put the Stripe key in the function. The gitleaks pre-commit hook catches this before it enters the git history. Install it once and it runs on every commit automatically.

2. Review every AI-generated API route for authentication

AI agents scaffold API endpoints quickly, often without adding authentication middleware if it was not specified in the prompt. After any AI session that creates new API routes, review each route and verify that a valid session token is required to access it. Endpoints that should be public should be explicitly labeled as such.

3. Add rate limiting explicitly to every endpoint

AI-generated apps almost never include rate limiting unless it was part of the prompt. Add it explicitly to login, signup, and any endpoint that triggers a third-party API call or expensive computation. At minimum: 5 requests per IP per minute on authentication endpoints, 60 requests per IP per minute on standard API endpoints.

4. Run dependency audit after every AI session

AI agents add npm packages or pip packages liberally. Run npm audit or pip-audit after every session that involved the AI installing new packages. Upgrade or replace any package with high or critical CVEs before shipping.

5. Test every new database query for authorization

AI agents write database queries that return results for a given ID. They do not always verify that the requesting user owns the resource identified by that ID. For every AI-generated query that returns user data, verify that the query includes a condition that restricts results to the requesting user. An unauthenticated call with a different user’s ID should return empty, not that user’s data.

6. Check HTTP security headers after deployment

AI builders do not configure security headers. Check after every deployment with the free LRC headers scanner. The required headers: Content-Security-Policy, X-Content-Type-Options, X-Frame-Options, Referrer-Policy, and Permissions-Policy. Missing headers can be added in one configuration block on your hosting provider.

7. Add error tracking before the first real user

AI-generated apps ship without error tracking by default. Install Sentry (or equivalent) and verify it is capturing errors before you share the app with real users. The install is typically five minutes. Without it, production failures are invisible until customers complain.

8. Run a URL-based scan after every significant deployment

The AI agent sees your code. A URL-based scan sees what your deployed app exposes. The two surfaces are different. Run the free LRC scan after any significant deployment to catch what the code-level tools missed: open endpoints, missing headers, rate limiting failures, absent monitoring. The full audit ($499) covers all four dimensions with specific findings and time estimates for each fix.

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