Is Lovable secure? What its security scan catches, and what it misses (2026)

Short answer: Lovable the platform takes security seriously, and its built-in scans catch many common problems. But the platform being secure is not the same as your app being secure. Lovable's own documentation says its tools "cannot guarantee complete security" and that you are "responsible for ensuring that your app meets the security requirements appropriate for its use case" (Lovable docs). The most common way Lovable apps leak data is still the database rules, and a scan passing doesn't prove those rules are right.

This guide explains what Lovable checks for you, where the real gaps are, and how to test your own app in about 20 minutes before you invite real users.

Why people are asking

Two public incidents made "is Lovable safe?" a common question:

• CVE-2025-48757 (2025). Security researcher Matt Palmer found that many Lovable-generated apps had missing or weak Row Level Security (RLS) on their Supabase databases. Anyone with the app's public key could read, and sometimes write, whole tables. His write-up counts 170+ affected projects and 303 vulnerable endpoints, with data such as emails, phone numbers, payment status and third-party API keys exposed (Matt Palmer, Superblocks summary). Lovable disputes the CVE, saying each customer is responsible for protecting their app's data (cvefeed).
• A showcased app in February 2026. A researcher reported 16 vulnerabilities in an app featured on Lovable's Discover page, with 18,697 records exposed. Lovable responded that its pre-publish scan had flagged issues, that fixing them was up to the app's creator, and that the vulnerable code wasn't Lovable-generated (The Register).

Both stories point the same way. The platform gives you tools and warnings, and the app owner has to act on them. That isn't a Lovable-only problem either: a scan of 5,600 public apps built with several AI tools found more than 2,000 vulnerabilities and 400+ exposed secrets (Escape).

What Lovable checks for you (as of late 2026)

Lovable has two scans, both reached from the Security view in your project (Lovable docs):

Lovable also warns you when you paste an API key into the chat, and offers optional integrations with Wiz and Aikido for deeper testing.

That's a good set of tools, and you should use all of it. Run a Deep Scan before every launch. It does not run by itself.

Where the gaps are

Scans are good at spotting patterns: a table with RLS switched off, a secret in the code, an outdated package. They are weaker at judging whether your rules match what your app is supposed to allow. These are the problems that most often slip through, and the ones we check first:

1. RLS is on, but the policy is wrong

A table can have Row Level Security enabled and still be wide open. A policy like using (true) or using (auth.role() = 'authenticated') passes a "is RLS enabled?" check, yet lets any logged-in user read every row. Palmer's write-up notes that Lovable's earlier scanner checked that RLS existed, not that the policies were sufficient (Matt Palmer). Deep Scan is better at this now, but only you know whether user A should ever see user B's orders.

Watch for: profiles, orders, messages, bookings or invoices tables where the select policy doesn't compare a column to auth.uid().

2. Users can change fields they shouldn't

Even with a correct "users can update their own row" policy, a user may be able to update every column in that row, including role, is_admin, plan or credits. That's a one-request upgrade to admin or a free paid plan.

Fix: keep privileged fields in a separate table that only the server can write, or restrict updates with column-level privileges or a trigger.

3. "Paid" is decided in the browser

If the app marks a user as paid because the browser says checkout succeeded, anyone can fake it. Payment status should only change when your server receives a verified webhook from Stripe or Paddle.

4. Edge Functions that trust the caller

Supabase Edge Functions often do the powerful work (sending emails, calling OpenAI, charging cards) with elevated keys. If a function doesn't check who is calling and what they're allowed to do, anyone can call it directly, running up your AI bill or reading other users' data. This matters more in 2026 because functions using Supabase's new API keys need verify_jwt = false and must check auth in their own code (Supabase migration guide). See our guide to Supabase's new publishable and secret keys.

5. Storage buckets set to public

Uploaded files (ID photos, invoices, medical documents) in a public bucket can be downloaded by anyone who has or guesses the URL. Scans flag some of these, but whether a bucket should be public is a business decision.

6. Things outside Lovable

The scan sees your Lovable project. It doesn't see the Stripe dashboard, your domain's DNS, a Zapier automation, or a second Supabase project a freelancer connected. If code was edited outside Lovable (for example in Cursor, via GitHub sync), the February 2026 incident shows responsibility gets murky fast.

A 20-minute test you can run yourself

You don't need to be a developer for this. You need two browsers (or one normal and one private window) and your app's live URL.

1. Make two test accounts, User A and User B, with emails you control.
2. As User A, create some data: a profile, an order, a message, an upload.
3. As User B, open developer tools (F12 → Network tab) and use the app normally. Click through every page.
4. Look at the responses from requests going to supabase.co/rest/v1/.... Click one and open the Response tab. Do you see User A's data, or anyone else's? If yes, you have an RLS problem.
5. Log out and paste private page URLs (dashboard, settings, admin) into the address bar. You should be sent to the login page, and no private data should load in the Network tab.
6. Search your site's JavaScript for secrets. In developer tools, open Sources, press Ctrl+Shift+F (Cmd+Option+F on Mac) and search for service_role, sb_secret_, sk_live, sk- and secret. A Supabase anon or publishable key is normal to see. The others should never be in the browser.
7. Try to buy without paying. If you have payments, use Stripe's test mode and cancel checkout halfway. Does the app still give you the paid features?

If any of these fail, don't panic, and don't keep prompting the AI blindly. Write down exactly what you saw, then fix one issue at a time and re-run the test. Lovable's own security pitfalls guide is a good next read.

So, should you use Lovable?

Yes, if it fits what you're building. It's one of the fastest ways to go from idea to working product, and its security tooling is better than most AI builders. Just treat the scan as a smoke alarm, not a building inspection. Before you take payments or store anything personal, have someone check that your database rules and server functions do what you think they do. For sensitive apps, Lovable's documentation itself suggests "an additional professional security review" (Lovable docs).

Want a second pair of eyes? PixelRidge offers a free Launch Check: send us your live link and we'll record a short video showing the top risks we can see, with your permission and without touching real user data. If you want everything checked properly, our Launch Readiness Audit gives you a written report and a fixed price for every fix. Request a Launch Check.

Sources are linked throughout. Lovable's features change often; this article reflects Lovable's documentation as of September 2026.