TL;DR
You built something real with AI. That's impressive. But the same tools that let you ship fast also introduce security blind spots. 45% of AI-generated code contains vulnerabilities. The good news: most are fixable once you know where to look.
You've shipped. Maybe it's an MVP, maybe it's already getting users. Either way, you built something functional with Cursor, Bolt, Lovable, or another AI coding tool. That's a real achievement.
Now comes the part nobody talks about when hyping up vibe coding: the security implications of all that AI-generated code running your business.
45% of AI-generated code contains security vulnerabilities according to Veracode's State of Software Security 2025 report.
This isn't meant to scare you or invalidate what you've built. It's meant to give you a realistic picture of what's probably in your codebase so you can do something about it.
Why AI-Generated Code Has Security Gaps
AI coding tools are optimized to give you working code fast. Security is a secondary concern, if it's a concern at all. Here's why vulnerabilities slip through:
The training data includes insecure patterns
AI models learn from millions of public repositories. Many contain outdated practices, deprecated methods, and outright security flaws. When the AI generates code, it doesn't distinguish between "this works" and "this is secure." It just gives you something that runs.
Functionality wins over safety
Ask for a login form, and you'll get one that works. But it might not hash passwords properly. It might not implement rate limiting. It might be vulnerable to brute force attacks. The AI solved the problem you asked about, not the problems you didn't know to ask about.
Context is missing
The AI doesn't know if you're building a side project or handling sensitive customer data. It doesn't know your compliance requirements. It uses generic patterns that may be wildly inappropriate for your actual security needs.
Speed creates blind spots
When you can ship in hours instead of months, security reviews feel like unnecessary friction. "I'll fix it later" becomes the default. Later never comes, and now you have users on a system you never properly secured.
The Vulnerabilities You Probably Have
After analyzing hundreds of vibe-coded applications, clear patterns emerge. These are the issues we see repeatedly:
| Issue | How Common | Why It Matters |
|---|---|---|
| Exposed API keys in frontend code | Very Common | Anyone can view source and steal your keys. Leads to financial loss and data breaches. |
| Missing Row Level Security (RLS) | Very Common | Users can access other users' data. One API call exposes your entire database. |
| Weak authentication | Common | Account takeovers, unauthorized access, impersonation attacks. |
| Missing security headers | Very Common | Opens doors to XSS, clickjacking, and other browser-based attacks. |
| Exposed .env files | Occasional | Complete system compromise. All your secrets in one discoverable file. |
| No HTTPS enforcement | Occasional | Data interception, session hijacking, credential theft. |
Notice something about this list? These aren't sophisticated zero-day exploits. They're configuration mistakes and oversights. Things that are fixable once you know they exist.
The Real Risk Isn't Technical
The technical vulnerabilities are fixable. The real risk is what happens when they're exploited before you fix them:
- User trust evaporates instantly. One data breach notification email and your users are gone.
- Financial damage compounds. Stolen API keys can rack up thousands in cloud bills overnight.
- Legal exposure grows. GDPR, CCPA, and other regulations don't care that you used AI to build your app.
- Investor confidence drops. Due diligence will uncover security issues. Better they find fixes than vulnerabilities.
The founders who get burned are usually the ones who assumed security could wait. It can't.
Making Vibe Coding Secure
Security doesn't have to slow you down. It just has to happen. Here's the practical approach:
1. Accept the baseline
Start from the assumption that your AI-generated code has vulnerabilities. This isn't pessimism, it's statistics. Accepting this makes you more likely to actually check.
2. Scan before you ship
Automated security scans catch the obvious problems in minutes. Run one before you deploy. Run one after major changes. Make it part of your process, not an afterthought.
3. Fix critical issues immediately
Exposed secrets, missing authentication, database access controls. These can't wait. Everything else can be prioritized, but these need immediate attention.
4. Ask AI about security explicitly
The AI won't think about security unless you tell it to. When generating code, ask: "Is this implementation secure? What vulnerabilities might exist?" You'll get better output.
5. Build security into your workflow
Security isn't a one-time task. Every new feature, every deployment is a chance to introduce new issues. Regular scans and security-conscious prompting prevent accumulation.
Security Checkpoint
- Run a security scan on your current codebase
- Search your frontend code for API keys and secrets
- If using Supabase, verify RLS is enabled on all tables
- Check that authentication requires proper credentials
- Confirm HTTPS is enforced (no http:// access)
- Verify .env files are in .gitignore
- Review your deployment's security headers
- Add "make this secure" to your AI prompts
The Bottom Line
Vibe coding changed who can build software. That's genuinely revolutionary. But the same abstraction that makes development accessible also hides security complexity you need to address.
The 45% vulnerability statistic isn't a condemnation of AI tools. It's a reality check. These tools are optimized for speed and functionality. Security requires explicit attention.
You've already done the hard part: you built something. Now protect it.
Why does AI-generated code have security vulnerabilities?
AI models learn from millions of code repositories, including code with security flaws. They optimize for functionality rather than security, often using deprecated methods, skipping input validation, or using insecure defaults. The AI doesn't know your specific security requirements unless you explicitly ask for secure implementations.
Can vibe coding be done securely?
Yes, vibe coding can be secure with the right practices. Running security scans before deployment, asking AI explicitly about security during generation, and fixing critical issues like exposed API keys and missing authentication significantly reduces risk. Most vulnerabilities in vibe-coded apps are configuration mistakes that anyone can fix.
What are the most common security issues in vibe-coded apps?
The most common issues are exposed API keys in frontend code, missing Row Level Security on databases like Supabase, weak or missing authentication, lack of HTTPS, missing security headers, and exposed environment variables. These are usually configuration mistakes rather than sophisticated vulnerabilities.
How do I know if my vibe-coded app has security issues?
Run an automated security scan on your codebase. These tools can identify exposed secrets, missing security configurations, and common vulnerabilities in minutes. Most issues are detectable without deep security expertise and fixable with the right guidance.
Find Out What's In Your Code
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