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- Outdated dependencies with known vulnerabilities that were never updated after initial generation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 moreAI App Rescue
Take your Lovable, Bolt, or Cursor prototype to production. We fix auth, database architecture, and performance in 2 weeks.
Worried your app has some of these?
Our AI App Rescue package includes a security review covering all ten of these issues, with fixes included.
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