What it checks
12 risk categories
Paste a GitHub repo and catch the risky stuff AI coding tools miss: secrets, auth gaps, input safety, rate limits, privacy, and launch trust.
What it checks
12 risk categories
Good for
pre-launch trust
Output
grade + fix list
Best fit
MVPs built fast
This is not a giant enterprise platform. It is the fast, understandable pre-launch check for people shipping with Cursor, Claude, Lovable, Replit, Bolt, Vercel, Supabase, and starter kits they barely had time to review.
You need to know whether your first release is safe enough for real users, not spend three days learning a security suite.
The code works, but that does not mean the auth flow, rate limiting, headers, or secrets handling are good enough.
Use it as a fast first pass before a deeper manual review, especially when clients hand over half-finished AI-built codebases.
Critical
Find obvious credential leaks, weak auth wiring, and user input paths that look unsafe.
High impact
Catch the missing basics that turn “quick MVP” into “easy target” once traffic shows up.
Trust layer
Surface missing policies and weak engineering signals that make launches harder to trust.
Yes. The report is written in simple English and points at the biggest launch-risk issues first.
No. It means strong signals were found. It is a smart first check, not a full manual security audit.
Yes. The app supports GitHub connection for private repositories.