10 Common Security Vulnerabilities in AI-Generated Code (and How to Fix Them)
The most common security issues we find when auditing AI-generated apps — and what to do about each one.
AI coding tools are remarkably good at making code that works. They're much less consistent about making code that's safe — and the failure modes are often invisible until someone goes looking for them. Here are the ten issues we see most often when auditing AI-generated apps, grouped by theme.
None of these are unique to AI-generated code — they're classic web security issues. What's different is how often they show up by default, simply because the AI prioritized a working demo over a secure one.
Access Control Issues
The most common, and most serious, category. AI-generated apps frequently assume that if a user is logged in, they should be able to access whatever the UI lets them click through to. That assumption rarely holds once an app has more than one user.
- Missing or overly permissive Row-Level Security (RLS) policies on database tables
- API routes that don't verify the requesting user owns the resource they're requesting
- Admin or internal routes reachable by any authenticated user, not just admins
Data Exposure Issues
Sensitive information ending up somewhere it shouldn't — in the browser, in version control, or in error messages — is the second most common pattern.
- API keys or secrets committed directly into the repository or exposed in client-side code
- Sensitive fields, like internal IDs, emails, or pricing data, included in API responses that don't need them
- Verbose error messages that reveal stack traces, file paths, or database structure to end users
Authentication Issues
Authentication often "works" in the sense that login and signup function correctly — but the policies around it are frequently left at permissive defaults.
- Weak password requirements or no password strength feedback
- No rate limiting on login or password reset endpoints, leaving them open to brute-force attempts
Input & Dependency Issues
The last category covers how the app handles data coming in — from users, and from the packages it depends on.
- User input passed directly into queries or rendered without sanitization
- Outdated dependencies with known vulnerabilities that were never updated after initial generation
Frequently Asked Questions
Does this mean AI-generated code is inherently insecure?
Not inherently — but it's optimized for getting something working, not for anticipating misuse. The underlying frameworks, like React, Supabase, and Next.js, are secure when configured correctly; the configuration is usually what's missing.
How do you find these issues?
A combination of automated scanning for known patterns and a manual review of access control logic, database policies, and authentication flows — the kind of review that's hard to fully automate.
Can these be fixed without a rewrite?
Yes, almost always. These are configuration and logic fixes within the existing codebase, not architectural rewrites.
Related Reading
Lovable Production Checklist
Where access control and security fit into a full launch checklist.
Learn moreReal Authentication for Supabase + Lovable
A deep dive on the access control issues described above.
Learn moreAI App Rescue by Tool
Tool-specific issues and fixes for every AI builder we support.
Learn moreWorried your app has some of these?
Our AI App Rescue package includes a security review covering all ten of these issues, with fixes included.
View AI App Rescue Package