Answer the data-handling checklist
Rate each item across database access, input validation, secrets & config, access control, data protection, and privacy & disclosure as Yes, Partial, or No.
CURSOR / GITHUB COPILOT / REPLIT / LOVABLE COMPARISONS
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Answer a checklist about how your AI-generated app handles data — database access, input validation, secrets, access control, encryption, and privacy — grouped by category, and get a weighted risk score with the gaps that need fixing before real user data touches this app.
- Weighted scoring across 6 data-security categories
- Highlights your top data-risk gaps, worst first
- All answers stay in your browser
Answer the checklist and click Calculate risk score to see your risk tier and gap breakdown.
0
out of 100, weighted across all categories
How it works
We calculate a weighted risk score
Each item is weighted by how directly it protects real user data, giving a transparent 0-100 score and a risk tier from critical to low.
See your top data-risk gaps
The five weakest, highest-weight gaps are surfaced first — the specific things worth fixing before this app touches real user data.
Continue learning: How AI-generated code can expose sensitive data · Can AI-generated code introduce SQL injection risks?
Frequently asked questions
Does this scan my actual database or code?
No — this is a self-assessment checklist. It scores risk purely from the answers you provide and runs entirely in your browser; it does not connect to any database, API, or codebase.
Why is row-level access control weighted so heavily?
AI-generated apps commonly implement authentication but skip authorization — checking who a user is but not whether they should see this specific row. That gap lets one user read another user's data, which is why it carries a top weight.
What is the difference between authentication and authorization checks in this checklist?
Authentication confirms who is making the request; authorization confirms whether that specific user is allowed to access the specific data being requested. Both are checked separately because AI-generated code often implements one without the other.
Is a low score a reason to panic?
No — most AI-generated apps start with real data-handling gaps, especially around row-level access control and secrets in frontend code. The score is meant to focus a hardening pass before real user data is at risk.
How is this different from a security scanner or penetration test?
A scanner or pentest inspects real code, traffic, and infrastructure. This tool is a structured self-assessment checklist — faster and free, but only as accurate as your answers; it does not replace a professional security review before handling sensitive data at scale.
How We Compare
| Feature | MVPHub | Lovable | Cursor |
|---|---|---|---|
| Weighted data-risk score across 6 categories | Included | Not included | Not included |
| Flags top data-risk gaps with plain-language help | Included | Not included | Not included |
| Instant and free | Included | Not included | Included |
| AI app generation with a live database | Not included | Included | Included |
Lovable and Cursor help generate the database access code and API endpoints themselves, but neither scores whether that data handling is actually safe for real users. MVPHub gives a fast, structured self-assessment across the categories most often mishandled in AI-generated apps.
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