Is Your AI-Generated Code a Security Breach Waiting to Happen?

Last month I reviewed a startup’s entire backend. Built in eleven weeks. Sixty-something endpoints, Postgres, Redis, Stripe integration, the works. The CTO was proud—and honestly, he should have been. His three-person team shipped what used to take twelve engineers a quarter. 

They’d built it almost entirely with Claude Code and Cursor. 

Then I ran an AI-generated code security audit on it. 

Within four hours I found hardcoded Stripe API keys in two controllers, a JWT implementation that never verified the signature (yes, really), SQL queries assembled with string concatenation in three separate modules, and an admin endpoint that had zero authentication. Not broken authentication. No authentication. 

Nobody had written any of that code with malicious intent. The AI coding assistants generated it. The developers accepted it. And nobody caught it because the team was moving too fast to review what the machine was writing. 

This isn’t a rare story. I see some version of it almost every week now. 

Why Startups Can’t Stop Shipping with AI Coding Assistants 

I get it. I genuinely do. If you’re a startup CTO or tech co-founder running a small team, the economics of AI-assisted development are irresistible. Tools like Claude Code, GitHub Copilot, Cursor, ChatGPT, and various vibe coding workflows let a two-person engineering team produce output that rivals a team three times its size. 

You’re pre-seed or early-stage. You’ve got 18 months of runway, maybe less. Every week you don’t ship is a week your competitor gets closer to eating your lunch. AI coding assistant security isn’t the thing keeping you up at night—getting to product-market fit is. 

So you lean into it. You accept the AI-generated suggestions. You merge the pull requests. You ship. 

And most of the time, it works. The features land. Users sign up. Things feel great. 

The problem is what you can’t see. 

The Uncomfortable Truth: Insecure AI-Generated Code Ships Quietly 

Here’s what I’ve learned from doing code review for AI generated code across dozens of startups: LLM-generated code security is fundamentally different from human-written code security. Not because the vulnerabilities are exotic. Actually, it’s the opposite. 

AI-written code vulnerabilities tend to be textbook problems—the kind you’d find in OWASP Top 10 lists, in entry-level security courses. The kind that should be easy to catch. But here’s why they slip through: 

• AI models optimize for working code, not secure code. The model wants to give you something that runs, not something that’s hardened. 

• AI-generated code often follows patterns from training data that includes insecure examples. Stack Overflow is full of shortcuts that work but aren’t safe. 

• When you’re vibe coding—moving fast, iterating rapidly—you’re in build mode, not review mode. Your brain literally isn’t looking for flaws. 

• LLMs don’t have context about your threat model, your compliance needs, or your attack surface. They generate generic solutions to specific problems. 

The result? Your production codebase silently accumulates security debt. Not the kind that crashes your app. The kind that lets someone exfiltrate your user database six months from now. 

10 Real Vulnerabilities I Keep Finding in AI-Written Codebases 

These aren’t hypothetical. Every one of these comes from actual AI code security scans I’ve performed on real startup codebases in the past six months. I’m not naming companies, but I am being specific about the patterns.

1. Hardcoded Secrets and API Keys

This is the most common thing I see. Claude Code and Copilot will happily generate code with your Stripe secret key, your database connection string, or your JWT signing secret sitting right there in a controller file. The model doesn’t know it should use environment variables—it just wants to produce code that works. I’ve found AWS keys, SendGrid tokens, and even OAuth client secrets hardcoded in plain text. 

2. Weak or Nonexistent JWT Validation

I’ve seen AI-generated authentication code that decodes JWTs without verifying the signature. Just pulls the payload out and trusts it. In one case the code accepted tokens signed with ‘none’ as the algorithm. That’s not a subtle bug—it’s a front door left wide open. 

3. Insecure Password Storage

AI models sometimes generate code that stores passwords in plain text or uses outdated hashing (MD5, SHA1 without salt). I reviewed a codebase last quarter where the AI had implemented bcrypt correctly on the signup endpoint but used plain MD5 on the password reset flow. Different prompts, different outputs, inconsistent security.

4. SQL Injection via String Concatenation

This one never gets old. Despite decades of parameterized queries being standard practice, AI coding tools still generate SQL with string concatenation. Especially in more complex queries with dynamic filters or sorting. The simple CRUD operations usually get parameterized. The complex stuff? String concatenation. Every time.

5. Missing Rate Limiting

Almost no AI-generated code includes rate limiting. Not on login endpoints, not on API routes, not on password reset flows. It’s just not in the model’s default output. This leaves your app wide open to brute force attacks and credential stuffing.

6. Broken Access Control

This is an OWASP Top 10 stalwart, and AI makes it worse. I regularly find endpoints where the code checks if you’re authenticated but never checks if you’re authorized. User A can access User B’s data just by changing an ID in the URL. The AI generates the auth middleware but doesn’t generate the authorization logic for individual resources.

7. Unsafe File Upload Handling

AI-generated file upload code frequently lacks proper validation. No file type checking beyond the extension. No file size limits. No content-type verification. No sanitization of filenames. I found one case where uploaded files were served directly from the filesystem with executable permissions. That’s a remote code execution vulnerability sitting right there.

8. Insecure CORS Configuration

Copilot and Claude Code love generating CORS configs that allow all origins. The classic Access-Control-Allow-Origin: * with credentials. I see it in probably 70% of the codebases I review. It works in development, it ships to production, and nobody questions it.

9. Dependency and Supply Chain Risk

AI suggestions frequently pull in packages that are unmaintained, deprecated, or have known vulnerabilities. The model doesn’t check if a package is still actively maintained or if it has CVEs. It just knows the package exists and solves the problem. I’ve found AI-suggested dependencies with critical vulnerabilities that were publicly disclosed months before the code was generated.

10. Logging Sensitive Data

This one’s sneaky. AI-generated error handling and debugging code often logs request bodies, including passwords, tokens, credit card numbers, and PII. Great for debugging. Terrible when those logs end up in CloudWatch, Datadog, or wherever you’re shipping them. It’s a data compliance nightmare and a frequent OWASP risks AI generated code exhibits. 

Claude Code Vulnerabilities I’ve Personally Seen 

I want to be specific here because I think Claude Code is actually one of the better AI coding tools. But ‘better’ doesn’t mean ‘secure.’ Here are Claude Code vulnerabilities that came up in real engagements: 

In one project, Claude Code generated a Node.js middleware stack where the error handler caught exceptions and returned the full stack trace to the client in production. Including internal file paths and database connection details. The developer had prompted Claude to ‘add error handling,’ and Claude delivered exactly that—error handling that handled errors by showing them to the world. 

In another case, Claude generated a file storage service that used predictable, sequential filenames. If you uploaded something and got back file_1042.pdf, you could just guess file_1041.pdf and access someone else’s document. No access control on the storage layer at all. 

A third example: Claude built an OAuth implementation that stored the access token in a cookie without the HttpOnly flag. The token was accessible to any JavaScript running on the page, which means any XSS vulnerability—and there were a few—gave an attacker full access to the user’s session. 

None of these are Claude being ‘bad.’ They’re Claude optimizing for functionality over security, which is exactly what these models are trained to do. The problem is on our side: we trust the output without verification.

Vibe Coding Security Risks Nobody Talks About 

There’s a cultural layer to this problem that goes beyond the code itself. The vibe coding movement—which I’m broadly a fan of, by the way—creates a set of security issues in AI-generated backend code that are almost invisible because they’re cultural, not technical. 

Speed over review. 

When you’re in a flow state shipping features with AI, the last thing you want to do is stop and carefully review every generated function. The whole point of vibe coding is momentum. But security requires deliberate friction. Those two goals are in direct tension. 

No threat modeling. 

Fast-moving teams using AI almost never sit down and think about attack vectors before building. They prompt, generate, test, ship. Threat modeling doesn’t fit the workflow. 

Overconfidence in the AI. 

There’s a subtle psychological thing happening: if a smart AI wrote the code, it must be good, right? I’ve talked to developers who admitted they review AI-generated code less carefully than human-written code. That’s backwards, but it’s human nature. 

Copy-paste across projects. 

AI-generated patterns get copy-pasted from one project to another. One insecure pattern can propagate across your entire codebase, or across multiple codebases if your team uses templates. 

Vibe coding security risks are compounding risks. They don’t cause a single big failure. They create an environment where many small failures accumulate until something breaks. 

Is AI Generated Code Safe? 

Let me be honest about this because I think the question deserves a straight answer: should startups trust AI coding tools? 

Yes—with guardrails. 

AI-generated code can absolutely be safe. But it is not safe by default. Left unchecked, it produces code that works but isn’t hardened. With the right review process, the right tooling, and occasional expert audits, you can ship fast with AI and still maintain a reasonable security posture. 

The key phrase there is ‘with guardrails.’ Here’s what that actually looks like in practice. 

The AI Code Audit Checklist: What Every Startup CTO Should Enforce 

This is the practical checklist I give to every CTO I work with. It’s not exhaustive, but if you do these things, you’ll catch 80% of what I typically find in an AI code security scan. 

1. Run SAST on every PR. Use static analysis tools (Semgrep, CodeQL, Snyk Code) in your CI pipeline. These catch hardcoded secrets, SQL injection patterns, and insecure defaults before code reaches production. 

1. Enforce dependency scanning. Tools like Dependabot, Snyk, or Socket catch known vulnerabilities in AI-suggested packages. Run these on every build, not just weekly. 

1. Mandate human review for auth and data flows. Any AI-generated code touching authentication, authorization, payments, or user data gets a dedicated human review. No exceptions. This is how to secure AI generated code in production. 

1. Use secrets detection pre-commit. Tools like GitGuardian or truffleHog catch secrets before they ever reach your repo. This is table stakes. 

1. Implement security headers and CORS properly. Don’t accept AI defaults for CORS, CSP, or other security headers. Define your policy explicitly and validate it. 

1. Add rate limiting to every public endpoint. The AI won’t do this for you. Manually verify rate limits on login, signup, password reset, and API endpoints. 

1. Audit file uploads. Verify file type, size, and content. Scan for malware. Never serve uploaded files with executable permissions. 

1. Log reviews for PII. Check your logging output for passwords, tokens, and personal data. Implement log scrubbing. 

1. Schedule quarterly AI generated code security audits. Bring in an outside set of eyes at least quarterly. Your team is too close to the code to see what an attacker sees. 

1. Document your AI usage. Track which parts of your codebase are AI-generated. AI code compliance starts with knowing what the AI wrote. 

Who This Is For 

If you’re reading this and nodding along, you’re probably one of these people: 

• A CTO or VP Engineering at a startup with 5–50 engineers, shipping fast with AI tools 

• A tech co-founder who’s been the one writing most of the AI-generated code yourself 

• A lead developer or engineering manager who knows the codebase needs a security pass but hasn’t had the bandwidth 

• Someone whose company is about to go through a SOC 2 audit, a customer security questionnaire, or a fundraising due diligence and is suddenly worried about what’s in the code 

If any of that sounds familiar, keep reading. 

What You Get with the AI-Generated Code Security Scan 

I built this offering specifically for startups using AI coding tools. It’s not a six-month consulting engagement. It’s not an enterprise security audit that costs $200K. It’s a focused, practical security scan designed for how early-stage teams actually work. 

Here’s what’s included: 

• Full codebase security scan focused on OWASP Top 10, insecure authentication, broken access control, secrets leakage, dependency vulnerabilities, and the AI-specific patterns I’ve described above. 

• Prioritized findings report that tells you what’s critical, what’s medium risk, and what can wait. No 200-page PDF you’ll never read. 

• Remediation guidance for every finding. Not just ‘fix this’—actual code-level guidance on how to fix it. 

• 30-minute walkthrough call where I go through the findings with your team, answer questions, and help you prioritize. 

• Startup-friendly pricing. I work with early-stage budgets. This isn’t designed to drain your runway. 

The whole thing is designed to be fast. Most scans are completed within a week. You get actionable results, not vague advice. 

Let’s Find Out What’s Actually in Your Codebase 

If you’re shipping AI-generated code to production—and at this point, most startups are—you need an outside set of eyes. Not because your team is incompetent. Because the AI tools are generating vulnerabilities your team isn’t looking for. How attackers exploit AI-generated code isn’t some theoretical concern. It’s a matter of when, not if. 

I’ve reviewed over 200 startup codebases built with AI coding assistants. I know exactly where the bodies are buried. And I can show you what’s in yours before someone else finds it first. 

If you want me to scan your AI-written codebase, book a call. 

You’re shipping fast. That’s great. Let’s make sure you’re not shipping a breach along with your features.