AI Generated Code Vulnerabilities: The Security Risks You Need to Know (2026)

You built something that works. Users can sign up, data saves, payments go through. Ship it, right?

Not so fast. The code behind that working app might be quietly exposing your users' data, leaking your API keys, or letting anyone with basic HTTP knowledge read your entire database.

This is not hypothetical. Beesoul, an agency that specializes in auditing vibe-coded apps, reports that most AI-built applications ship with 8 to 14 security findings. A separate scan of 200+ vibe-coded sites on Reddit found an average security score of just 52 out of 100.

Here is what is actually going wrong in AI-generated code, why it happens, and what you can do about it.

Why AI-Generated Code Has More Vulnerabilities

AI code generators are trained to produce code that works, not code that is secure. There is an important difference.

When you prompt Cursor or Lovable to "build a user dashboard with Supabase," the model focuses on making the feature functional. It creates tables, writes queries, renders data. The app works. But the model rarely considers who else might be able to access that data, whether the API keys are properly secured, or what happens when someone sends malformed input.

This is not a flaw unique to one tool. It is a structural pattern across AI code generation. The models optimize for the visible request (make it work) and under-prioritize the invisible requirements (make it secure). Human developers make similar mistakes, but they typically have years of scar tissue from past security incidents. AI models do not accumulate caution the same way.

The result: Damian Galarza found 69 vulnerabilities across just 15 AI-built apps in his security assessments. That is nearly 5 vulnerabilities per app on average, and these were apps their founders considered ready to ship.

The 7 Most Common Security Risks in Vibe-Coded Apps

Based on audit data from Beesoul, Varyence, and open-source scanner results, these are the vulnerabilities that show up repeatedly.

1. Disabled Row-Level Security (RLS)

This is the single most common issue. Beesoul reports that roughly 70% of Lovable-built apps ship with RLS disabled on Supabase tables. When RLS is off, any authenticated user can read, modify, or delete any other user's data with a simple API call.

Your app looks normal from the frontend. But the database is wide open.

2. Hardcoded Secrets and API Keys

AI models frequently embed API keys, database connection strings, and service tokens directly in source code. These end up in your Git history, your client-side bundle, or both. Even if you later move them to environment variables, the keys remain in your commit history unless you specifically scrub them.

3. Missing Webhook Verification

If your app processes payments through Stripe, receives notifications from third-party services, or handles any external callbacks, those webhook endpoints need signature verification. AI-generated code almost never includes this. Without it, anyone can send fake webhook payloads to your endpoint and trigger actions (like marking orders as paid) without actually paying.

4. No Soft Deletes

When a user deletes their account or data, AI-generated code typically runs a hard DELETE query. The data is gone permanently. This creates problems for GDPR compliance (you need to prove what you deleted and when), for billing disputes (no audit trail), and for accidental deletion recovery.

5. N+1 Database Queries

This is more of a performance vulnerability than a security one, but it can lead to denial-of-service conditions. AI-generated code frequently fetches related data in loops rather than using joins or batch queries. Under load, this can make your database unresponsive, taking down the entire app.

6. Exposed Internal Error Messages

When something goes wrong, AI-generated code often returns the raw error message to the user. These messages can reveal your database schema, table names, column types, and internal logic. That is a roadmap for attackers.

7. Missing Input Sanitization

AI models generate code that trusts user input. Form fields, URL parameters, and API request bodies get passed directly to database queries or rendered in HTML without sanitization. This opens the door to SQL injection and cross-site scripting (XSS) attacks.

Real Numbers From the Field

These are not theoretical risks. Here is what auditors are actually finding.

The GrowExx case is particularly telling. They audited a production SaaS built with Claude Code that passed all automated linters and tests. The 48-hour deep audit still uncovered risks that no automated tool had flagged. Linters check syntax. They do not check whether your business logic exposes data it should not.

Why AI Cannot Reliably Audit Its Own Code

There is a tempting shortcut: ask the AI to review its own output. Some developers are doing this, and it does catch certain issues. But it has a consistent blind spot.

The NetSPI experiment tested this directly. They vibe-coded an app, then had the AI audit it, implemented the AI's fixes, and finally ran a human penetration test. The pentest still found vulnerabilities the AI had missed.

The X/Twitter community reflects this finding. As one developer put it: "Vibe coding without a security audit is not a calculated risk. It is negligence." Others are more blunt: "You cannot vibe code audit."

The core issue is that AI models lack context about your specific infrastructure, your threat model, and your business rules. They can check for common patterns (is RLS enabled? are there hardcoded strings that look like keys?) but they cannot reason about whether your particular webhook flow could be exploited, or whether your multi-tenant isolation actually works under concurrent load.

AI-assisted review is a useful first pass. It is not a substitute for human review on anything touching real user data or money.

How to Find These Vulnerabilities Yourself

You do not need to be a security expert to catch the most common issues. Here is a practical approach that takes 1 to 2 hours.

Step 1: Run a Free Scanner

The vibe-codebase-audit tool on GitHub is free and open source (MIT license). It scans for secrets, data exposure patterns, and common vulnerabilities specific to vibe-coded projects. You need an OpenRouter API key for the AI-assisted portion, but the static scans work without one.

Step 2: Check the Critical Five

Go through these manually. They account for the majority of serious issues:

1. RLS status: Open your Supabase dashboard, check every table. If RLS is not enabled, enable it and add policies before anything else.
2. Secrets in code: Search your codebase for API keys, tokens, and connection strings. Check .env files are in .gitignore. Review your Git history for previously committed secrets.
3. Webhook endpoints: Find every endpoint that receives external callbacks. Verify each one checks the request signature.
4. Error responses: Trigger errors in your app and check what gets returned to the browser. If you see database details, fix it.
5. Input handling: Test your forms and API endpoints with unexpected input. Try HTML tags in text fields. Try SQL-looking strings.

Step 3: Use AI as a Second Pass

After your manual check, use Cursor or Claude to review specific files for security issues. Ask it to focus on authentication flows, data access patterns, and input validation. Treat its findings as suggestions that need verification, not as definitive answers.

For a structured approach to this full process, see our vibe code audit guide.

When to Bring in a Professional

DIY audits catch the low-hanging fruit. For apps that handle any of the following, a professional audit is worth the investment:

• Payments or financial data
• Personal health information
• User-generated content that gets displayed to others
• Multi-tenant data where one customer's information must never leak to another

Professional security audit pricing in 2026:

Agencies like Beesoul and Varyence specialize in vibe-coded applications. They know the common patterns because they see them daily. A typical turnaround is 3 to 10 business days.

The math is straightforward. A data breach costs far more than a $1,500 audit. For context, the average cost of a data breach for small businesses is in the tens of thousands once you factor in notification requirements, lost customers, and potential regulatory fines.

Frequently Asked Questions

What are the most common vulnerabilities in AI-generated code? Missing input validation, exposed API keys, disabled or absent row-level security, SQL injection via unsanitized queries, and hardcoded credentials. These show up across every major AI coding tool.

Is AI-generated code less secure than human-written code? Not inherently, but AI tools optimize for "working code" rather than "secure code." Without explicit security prompts, they tend to skip defensive patterns that experienced developers include by habit.

Can AI tools audit their own generated code? Partially. Tools like Cursor and Copilot can flag some issues when asked, but they miss context-dependent vulnerabilities. Dedicated scanners (Semgrep, SonarQube) and human review catch what AI misses.

How much does it cost to fix AI code security issues? A basic security audit runs $500 to $1,500. Full remediation for a typical vibe-coded app costs $1,500 to $5,000 depending on severity and app complexity.

How do I check if my AI-generated app is secure? Start with free tools: run npm audit, use the vibe-codebase-audit scanner, check your Supabase RLS policies, and search your codebase for hardcoded keys. For anything handling user data or payments, hire a professional auditor.

Fix What You Find

Finding vulnerabilities is half the job. Here is where to go next:

• Security vulnerabilities: Our security vulnerabilities fix guide walks through remediation step by step
• Broken AI-generated apps: If your app has deeper structural issues beyond security, start with our guide to fixing AI-generated apps
• Production readiness: For a broader checklist covering performance, monitoring, and reliability alongside security, see our AI MVP to production guide

The goal is not to achieve perfect security. That does not exist. The goal is to close the obvious gaps so your app is not the low-hanging fruit that automated scanners and casual attackers target first.

Ship fast, but check the locks before you hand over the keys.

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Related reading:

• Vibe Code Audit: How to Review Your AI-Built App
• How to Fix an AI-Generated App
• Taking Your AI MVP to Production