You shipped the feature in 20 minutes. The demo looked perfect. Your stakeholders were impressed. Three weeks later, you’re explaining to your users why their data was exposed.

This scenario plays out constantly in the vibe coding era. The tools that let us build faster than ever also let us ship vulnerabilities faster than ever. After analyzing thousands of AI-generated codebases, I’ve identified five distinct traps that catch even experienced developers.

Trap 1: The Hallucinated Dependency Trap

AI models don’t just hallucinate facts. They hallucinate entire packages.

When you ask Cursor or Claude to add a feature, the AI might suggest importing a package that sounds perfect for your needs. The problem? That package might not exist. Or worse, it exists now because an attacker registered it after noticing AI models recommending it.

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// AI-generated code that looks legitimate
import { validateSchema } from 'express-schema-validator';

// This package was registered by an attacker
// after AI models started recommending it

The attack is elegant: researchers found that popular AI models consistently recommend certain non-existent package names. Attackers monitor these patterns and register the packages with malicious code. Your npm install becomes the attack vector.

How to escape this trap:

• Verify every dependency exists before installing
• Check package age, download counts, and maintainer history
• Use npm audit or tools like Socket.dev
• Question any package you haven’t heard of, even if the AI sounds confident

Trap 2: The “It Works” Trap

The most dangerous code is code that works perfectly in development.

AI models optimize for making code work. They’re trained on millions of examples of working code. But “working” and “secure” aren’t the same thing.

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// This works perfectly - users can log in
app.post('/login', async (req, res) => {
  const user = await db.query(
    `SELECT * FROM users WHERE email = '${req.body.email}'`
  );
  if (user && user.password === req.body.password) {
    res.json({ token: generateToken(user) });
  }
});

// But it has THREE critical vulnerabilities:
// 1. SQL injection via string interpolation
// 2. Plain-text password comparison
// 3. No rate limiting on login attempts

The demo looks great. The feature works. The vulnerability ships to production.

How to escape this trap:

• Never trust code just because it runs
• Review AI output with security-specific questions: “Where’s the input validation? Where’s the parameterization?”
• Run security scanners on every AI-generated function
• Assume the AI optimized for functionality, not security

Trap 3: The Happy Path Trap

AI models love the happy path. Your attackers don’t.

When you prompt an AI with “create a payment form,” it generates code that handles successful payments beautifully. What happens when the payment fails? When the network times out? When someone submits a negative amount?

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# AI-generated payment handler
def process_payment(amount, card_token):
    result = stripe.charges.create(
        amount=amount,
        source=card_token
    )
    return {"success": True, "charge_id": result.id}

# Missing: validation, error handling, logging
# What if amount is -100?
# What if the charge fails?
# What if stripe is unreachable?

The happy path trap is particularly insidious because the code looks complete. It handles the success case so elegantly that the missing error paths aren’t obvious until something goes wrong in production.

How to escape this trap:

• Always ask “what happens when this fails?”
• Prompt the AI specifically for error handling
• Test with invalid inputs, not just valid ones
• Add monitoring for edge cases

Trap 4: The Copy-Paste Trap

Every AI model was trained on code containing secrets. Sometimes those patterns leak out.

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# AI helpfully providing "example" configuration
AWS_ACCESS_KEY = "AKIAIOSFODNN7EXAMPLE"
AWS_SECRET_KEY = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

# These look like placeholders but attackers scan for them
# Some developers don't realize they need to change them

The copy-paste trap goes beyond just secrets. AI models reproduce insecure patterns they’ve seen thousands of times. That cors({ origin: '*' }) configuration appeared in so many tutorials that the AI treats it as standard practice.

How to escape this trap:

• Use environment variables for all configuration
• Scan commits for secrets before pushing
• Don’t trust AI-generated “example” values
• Set up pre-commit hooks to catch secrets

Trap 5: The Speed Trap

Vibe coding feels productive because it is productive. That’s also what makes it dangerous.

You used to spend a day implementing a feature. Now it takes 30 minutes. Your security review process was designed for the old pace. Something has to give, and usually it’s security.

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Traditional workflow:
Write code (8 hours) -> Review (2 hours) -> Ship
Security coverage: 20% of dev time

Vibe coding workflow:
Generate code (30 min) -> Ship immediately
Security coverage: 0% of dev time

The math is brutal. Even if AI-generated code has the same vulnerability rate as human code, shipping 10x faster means shipping 10x more vulnerabilities.

How to escape this trap:

• Automate security checks to match your new velocity
• Make security scanning part of your generation workflow
• Set a rule: no AI code ships without automated review
• Budget the time you saved for security validation

The Meta-Trap: Trusting the Vibes

All five traps share a common root: trusting that something feels right.

The hallucinated package sounds right. The working code looks right. The happy path seems complete. The example configuration appears standard. The shipping speed feels appropriate.

Vibe coding is called that because you’re trusting vibes instead of verification. That works for prototypes. It fails catastrophically for production.

FAQ

Conclusion

Vibe coding isn’t going away. It’s too productive to abandon. But the developers who thrive will be the ones who recognize these traps and build systems to escape them automatically.

The best vibe is shipping fast and staying secure. That takes more than good feelings.