I vibe coded my app — is it actually secure?

Vibe coding — building software by describing what you want to an AI and accepting whatever it generates — has made it possible for non-developers to ship real products in days instead of months. Tools like Lovable, Bolt, and v0 have lowered the barrier to building so dramatically that thousands of founders are now running production apps on code they never read. That speed is genuinely valuable. But AI coding tools are optimized to produce code that runs, not code that is hardened against adversarial input. Security is rarely in the prompt, so it is rarely in the output. The result is a generation of apps that work beautifully in the happy path and collapse the moment someone tries something the founder didn't anticipate.

There are three categories of security controls that almost every AI-built app is missing. The first is authentication on internal routes. When you ask an AI builder to add a dashboard, it generates the dashboard UI and a route that serves it, but it often skips the server-side session check that ensures only logged-in users can reach that route. The frontend navigation might hide the link, but the URL is still publicly accessible to anyone who types it directly. The second is input validation. AI tools generate forms that submit data and API routes that accept it, but they frequently skip sanitizing that data before using it in database queries or passing it to external services. The third is rate limiting. Every login form, contact form, and API endpoint in an AI-generated app is open to automated abuse by default because rate limiting requires explicit configuration that no one put in the prompt.

The fundamental gap is between "it works" and "it's safe". Working code passes the happy path — the user fills in the form, the data saves, the email sends. Safe code also passes the adversarial path — the attacker sends a malformed payload, submits the form a thousand times, or navigates directly to a route they're not supposed to reach. AI tools are excellent at generating code for the first kind of test and systematically skip the second. This is not a failure of the tools; it is a mismatch between what they optimize for and what production security requires. A founder who ships an AI-built app and assumes it is secure because it passed their own tests is testing for the wrong thing.

Doing a fast security self-audit does not require hiring a penetration tester. Start with your HTTP headers: open your deployed app in a browser, open DevTools, look at the response headers on any page request. You should see Content-Security-Policy, X-Frame-Options, Strict-Transport-Security, and X-Content-Type-Options. If any of those are absent, a scanner will flag it and so will an attacker. Next, check for exposed keys: open the Sources tab in DevTools and search for strings like sk_, pk_, and Bearer. If you find a key that should be private appearing in your client-side JavaScript bundle, rotate it immediately. Then check your Supabase dashboard: every table should have Row Level Security enabled, and your service role key should not appear anywhere in your client-side environment variables.

Sayver automates this entire audit process. It scans your live URL for missing security headers, checks your source code for exposed secrets and common SAST vulnerabilities, and monitors incoming traffic for signs of attack in real time. Every finding is explained in plain English with a specific fix — the kind you can paste directly into your AI builder and get working code back. For founders who are shipping fast and don't have a security background, it bridges the gap between an AI-built app and a production-ready one. Running a scan before you share your launch link is the single highest-leverage security action you can take.