TL;DR
You built something real with AI, and the same tools that let you ship that fast also left blind spots behind. Veracode puts vulnerabilities in 45% of AI-generated code. Most of them are fixable in an afternoon once you know where to look.
You've shipped. Maybe it's an MVP, maybe it's already got users. Either way you built something functional with Cursor, Bolt, Lovable or another AI tool, and that's a real achievement.
Then comes the part nobody mentions in the vibe coding pitch: all of that generated code is now running your business.
45% of AI-generated code contains security vulnerabilities according to Veracode's State of Software Security 2025 report.
That number isn't there to scare you. It's a realistic picture of what's probably sitting in your codebase right now, which is the only useful starting point for doing something about it.
Why AI-Generated Code Has Security Gaps
AI coding tools are optimized to hand you working code fast. Security is a secondary concern, when it's a concern at all. Four reasons vulnerabilities slip through:
The training data includes insecure patterns
AI models learn from millions of public repositories, and plenty of those repositories are full of deprecated methods and outright security flaws. The model has no way to tell "this works" apart from "this is safe." It gives you something that runs.
Functionality wins over safety
Ask for a login form and you'll get one that works. Whether it hashes passwords properly is a coin flip, and rate limiting almost never appears unless you name it. The AI solved the problem you asked about. It has no opinion about the problems you didn't know to ask about.
Context is missing
The AI doesn't know whether you're building a weekend side project or handling medical records, and it has never heard of your compliance requirements. So it reaches for generic patterns that can be wildly wrong for what you're actually doing.
Speed creates blind spots
When you can ship in an afternoon instead of a quarter, a security review feels like pure friction. "I'll fix it later" becomes the default answer. Later never arrives, and by then there are real users on a system nobody ever 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. |
Look at what that list is made of. No zero-days, no exotic exploit chains, nothing that needs a researcher. Configuration mistakes, every one, and all of them fixable the moment you know they're there. The single most common thing we see in a scan is an API key sitting in a frontend bundle in plain text, on a site that has been live for months.
The Real Risk Isn't Technical
The vulnerabilities themselves are fixable. What isn't fixable is what happens if somebody finds them first:
- 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 version:
1. Accept the baseline
Assume your AI-generated code has vulnerabilities in it. That's not pessimism, it's just where the numbers sit, and assuming it makes you far more likely to go and check.
2. Scan before you ship
Automated scans catch the obvious problems in minutes. Run one before you deploy, and again after any change big enough to make you nervous. The goal is for it to become a habit rather than a special occasion.
3. Fix critical issues immediately
Exposed secrets, missing authentication, wide-open database access. Those three can't wait. Everything else can go in a queue and be worked through in order.
4. Ask AI about security explicitly
The model won't raise security on its own. Ask it directly: "Is this implementation secure? What vulnerabilities might exist?" The output changes noticeably, because you've made security part of the problem you asked it to solve.
5. Build security into your workflow
This is not a task you finish. Every feature and every deploy is another chance to introduce something, so regular scans and security-conscious prompting are what stop the debt piling up.
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 gets to build software, which is genuinely a big deal. The catch is that the same abstraction making development accessible also hides the security complexity underneath it.
Read the 45% as a reality check rather than a verdict on AI tools. They're built for speed, they're very good at it, and security is the part that only happens when someone asks for it.
You've already done the hard part. 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
Run a free security scan and get plain-English results about what needs fixing.