Cursor App Launch Security Checklist: 18 Items Before Going Live

Q: How long does a Cursor app security review take?

A thorough review using this checklist takes 30-60 minutes for a typical app. Complex applications with multiple integrations may take 1-2 hours. Automated scanning can reduce this time significantly.

TL;DR

Before launching your Cursor-built app, verify all AI-generated code for hardcoded secrets, test authentication on both client and server, check database security rules, and run an automated scan. This checklist covers the 18 most critical items that catch 90% of launch-day security issues.

Cursor makes building apps fast, but AI-generated code often prioritizes functionality over security. According to a 2025 Stanford study, 40% of AI-generated code contains at least one security vulnerability. This checklist helps you catch issues before users do.

API Keys and Secrets 5

Search all files for hardcoded secretsRun grep for: sk_, pk_, api_key, apiKey, secret, password, token, bearer

Verify .gitignore includes sensitive filesCheck for .env, .env.local, .env.production, and any config files with secrets

Confirm environment variables are set in deploymentCheck Vercel, Netlify, or your platform's environment variable settings

Test that no secrets appear in browserOpen DevTools Network tab and verify no API keys in requests or responses

Check git history for accidentally committed secretsUse git log -p to search history, or use a tool like truffleHog

Authentication and Sessions 4

Test all protected routes without authenticationTry accessing /dashboard, /admin, /settings, /api/* directly in incognito mode

Verify server-side authentication checksAuth must be validated on the server, not just hidden with client-side redirects

Test session expiration and logoutSessions should expire after inactivity, and logout should invalidate tokens

Check password reset flow securityReset tokens should expire and be single-use. No user enumeration on the form.

Database Security 4

Enable Row Level Security (Supabase)If using Supabase, RLS must be enabled on ALL tables, not just some

Test data isolation between usersLog in as User A, try to access User B's data by changing IDs in requests

Review all database queries for injection risksEnsure parameterized queries or ORM methods, no string concatenation

Verify database connection uses SSLCheck connection string includes ssl=true or equivalent

Input Validation 3

Test forms with malicious inputEnter <script>alert('xss')</script> in all text fields

Verify server-side validation existsClient validation can be bypassed. All inputs must validate on server.

Check file upload restrictions (if applicable)Validate file types, limit sizes, scan for malware, store outside web root

Pre-Launch Deployment 2

Enable HTTPS and verify it worksCheck that http:// redirects to https:// on all pages

Run an automated security scanUse CheckYourVibe or similar tool to catch issues you may have missed

Why Cursor Apps Need Extra Attention

Cursor's AI coding assistant speeds up development significantly. But the code it generates often takes shortcuts that work fine locally but create security holes in production. Common patterns include:

These aren't flaws in Cursor itself. They're a natural result of AI optimizing for "make it work" rather than "make it secure." Your job before launch is to add the security layer.

Common issues include hardcoded API keys, missing server-side validation, incomplete authentication implementation, and exposed database credentials. AI code generation often focuses on functionality over security, so manual review is essential before launch.

How long does a Cursor app security review take?

A thorough review using this checklist takes 30 to 60 minutes for a typical app. Complex applications with multiple integrations may take 1 to 2 hours. Automated scanning can reduce this time significantly.

Should I run a security scan before launching my Cursor app?

Yes, always run an automated security scan before launch. Manual review catches some issues, but automated tools find patterns humans miss, especially in larger codebases with many AI-generated files.

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