Can apps built with AI be hacked?
Yes, and the reason is consistent across tools. AI coding tools are very good at making a feature work and less focused on the settings that keep it safe. So the app runs perfectly and quietly skips things like database access rules, hidden keys, and security headers.
That means most real incidents are not sophisticated attacks. They are ordinary requests that succeed because a protection was never turned on: reading a table that has no access rules, copying a key that shipped to the browser, or downloading source that should not be public.
Because these are configuration gaps rather than deep flaws, they are both easy to find and easy to fix. A read-only scan of the live app shows which ones are present, and each comes with a plain-English fix.
Scan your AI-built app
What it means
Your database tables can be read by anyone, not just logged-in users. Someone could open your app's data — customer emails, orders, private records — without ever creating an account.
Why AI tools cause it
Supabase only protects a table once you turn on Row Level Security (RLS) and write access rules for it. AI tools frequently build a working app without doing that, so the tables are wide open by default.
How xlogs checks it
xlogs finds your public Supabase URL and anon key in your app (the same ones your frontend uses), asks the database as an anonymous user which tables it can read, and flags any that return real rows. Read-only: it never writes or deletes.
How to fix it
The goal: Row Level Security is enabled on every table, with policies so a table only returns rows to users allowed to see them; no table returns private data to an anonymous request.
- Enable Row Level Security (RLS) on every table, especially the affected spot
- Add a policy per table so only authenticated users can read/write their own rows (keep intentionally-public tables public on purpose)
- Re-check that an anonymous request no longer returns private rows
In my app (the spot the scan shows): anyone can read this database table. Please make this true: Row Level Security is enabled on every table, with policies so a table only returns rows to users allowed to see them; no table returns private data to an anonymous request. Steps: Enable Row Level Security (RLS) on every table, especially the spot the scan shows; Add a policy per table so only authenticated users can read/write their own rows (keep intentionally-public tables public on purpose); Re-check that an anonymous request no longer returns private rows. Then tell me exactly what you changed, and do not print any secret values back to me.
Then verify: After you deploy the policies, xlogs repeats the same anonymous read test and confirms the table no longer returns data without a login.
Full step-by-step fix guide, with a copy-paste block for each AI tool →
Common questions
Which AI coding tool is the most secure?
The differences are small, because the gaps come from the same skipped steps regardless of tool: access rules, key handling, and headers. What matters more is checking the app you actually shipped, since any of these tools can produce an app that works and still exposes data.