I Found 7 Security Flaws in My AI-Generated Blog

November 14, 2025 • by Jared Little • AI Learning

I Found 7 Security Flaws in My AI-Generated Blog

The Irony: I just published three blog posts about how amazing AI is for development. Then I ran a security audit and found 7 vulnerabilities in my AI-built blog.

The Lesson: AI is incredibly powerful, but you still need to verify security.

The Silver Lining: I used AI to fix all 7 vulnerabilities in under 10 minutes.

The Wake-Up Call

After deploying my blog (built entirely with Claude’s assistance), I checked the GitHub security tab:

⚠️ 7 vulnerabilities found
- 2 High severity
- 4 Moderate severity
- 1 Low severity

My reaction: “Wait, Claude helped build this. How did vulnerabilities slip through?”

What Went Wrong?

Let me be clear: Claude didn’t create malicious code. The vulnerabilities came from outdated dependencies.

The Problem: Unmaintained Package

When I asked Claude to “set up a blog with Astro,” it used broken-link-checker - a popular but unmaintained package from 2019.

{
  "devDependencies": {
    "broken-link-checker": "^0.7.8"
  }
}

Known vulnerabilities in its dependencies:

robots-txt-guard - Inefficient regex (HIGH)
tough-cookie - Prototype pollution (MODERATE)
form-data - Unsafe random function (CRITICAL)
tmp - Arbitrary file write (LOW)

Why AI Didn’t Catch It

Package worked perfectly (functionality ≠ security)
Popular package (100k+ downloads/week)
AI training data showed it as widely used
No syntax errors or obvious red flags

The Hidden Danger: Transitive Dependencies

broken-link-checker@0.7.8
└── bhttp@*
    └── tough-cookie@<4.1.3  ← VULNERABLE

Dependencies of dependencies are where vulnerabilities hide.

How I Used AI to Fix It (10 Minutes)

Here’s where it gets interesting: I used Claude to fix what Claude helped create.

The Conversation

Me: “I have 7 npm security vulnerabilities. Here’s the audit report. Analyze and recommend fixes.”

Claude’s Analysis:

broken-link-checker issues:
- Last updated: 2019 (UNMAINTAINED)
- Vulnerable dependencies: tough-cookie, form-data, tmp
- Deprecated dependencies: request, har-validator

Recommended: Replace with 'link-check'
- Actively maintained (updated 2024)
- Zero known vulnerabilities
- Modern codebase

Claude’s Solution:

npm uninstall broken-link-checker
npm install --save-dev link-check

Result: 7 vulnerabilities → 0 vulnerabilities ✅

Total time: 10 minutes

The “Trust But Verify” Framework

Here’s my new systematic approach:

Phase 1: Development (Trust AI)

Do:

Let AI generate code structure
Use AI for boilerplate
Leverage AI for common patterns
Trust AI for functional requirements

Don’t:

Blindly copy-paste to production
Skip understanding the code
Assume AI knows latest security patches
Trust AI for compliance decisions

Phase 2: Validation (Verify Everything)

Security Checks:

npm audit              # Check vulnerabilities
npm outdated           # Check for updates
snyk test              # Advanced scanning (optional)

Manual Review:

Read generated code
Check for hardcoded secrets
Verify environment variables
Review error handling
Test edge cases

Phase 3: Remediation (Use AI to Fix)

When issues are found:

Describe to AI: “I have these vulnerabilities: [paste report]. Analyze and recommend fixes.”
Evaluate recommendations: Do they make sense? Breaking changes? Better alternatives?
Implement with oversight: Let AI make changes, but review before committing
Re-run audits: Verify fixes worked

My Security Checklist

Before deploying any AI-assisted project:

Initial Setup:

Run npm audit
Check dependencies for maintenance status
Review package.json for unnecessary deps
Ensure no hardcoded secrets
Verify environment variables

Code Review:

Read AI-generated code
Understand all dependencies
Check input validation
Review auth/authorization logic
Verify error handling doesn’t leak info

Testing:

Run all tests
Test error cases and edge cases
Verify security headers
Check CORS configuration

Monitoring:

Set up Dependabot
Enable GitHub security alerts
Configure error monitoring

1. AI Doesn’t Know About Zero-Day Vulnerabilities

A package might be secure in AI’s training data but vulnerable today.

Solution: Always run current security scans.

2. AI Optimizes for “Working Code”

AI chooses packages that work, not necessarily the most secure.

Solution: Explicitly ask AI to prioritize security and maintenance status.

3. AI Doesn’t Monitor Your Production Code

New vulnerabilities appear after deployment.

Solution: Set up automated security monitoring (Dependabot, Snyk).

4. AI Can’t Judge Risk Context

A “low severity” vulnerability might be critical in your use case.

Solution: Understand your threat model and evaluate in context.

Questions to Ask AI About Security

Make AI your security assistant:

Before Choosing a Package:

Is [package-name] actively maintained?
When was it last updated?
Are there known security issues?
What are modern, well-maintained alternatives?

During Code Review:

Review this code for security vulnerabilities:
- Input validation
- Authentication logic
- Data exposure
- Dependency risks

When Fixing Issues:

I have this security vulnerability: [description]
What's the root cause?
What are the risks?
What's the best way to fix it?

The Paradox: AI Made Me More Security-Conscious

Before AI: I spent so much time on syntax and docs that security became an afterthought.

With AI: Since AI handles tedious parts, I have more time for security reviews.

The Irony: AI tools made me a more thorough developer, not a lazy one.

My Evolved Workflow

Before:

Ask AI to build feature
Review code quickly
Deploy if it works
Move on

After:

Ask AI to build feature
Review code thoroughly
Run security audits
Check dependency maintenance
Test edge cases
Deploy with monitoring
Set up automated security checks

Extra time: 15-20 minutes per feature Value: Avoid security incidents, sleep better

Practical Takeaways

For Developers Using AI:

Always run security audits before deploying
Understand AI-generated code before using it
Check package maintenance status for all dependencies
Use AI to help fix security issues
Set up automated monitoring for production

For Teams Adopting AI:

Establish security checklists for AI-assisted code
Require human code review even for AI code
Set up CI/CD security scans as gates
Train developers on secure AI development
Document security standards AI should follow

Tools I Use

NPM Projects

Built-in:

npm audit          # Check vulnerabilities
npm outdated       # Check for updates

Third-Party:

npx snyk test                  # Comprehensive scanning
npx npm-check-updates -u       # Update packages

Automation

Dependabot (GitHub) - Automated dependency PRs
GitHub Security Alerts - Vulnerability notifications
Snyk - Continuous monitoring

The Bottom Line

What I Learned:

AI can indirectly introduce vulnerabilities
AI can also fix them very effectively
Security audits are non-negotiable
“It works” ≠ “it’s secure”

What Changed:

I now run npm audit on every project
I check package maintenance before adding dependencies
I explicitly ask AI about security considerations
I spend 15 extra minutes on security vs hours on incident response

The Meta-Lesson: Using AI to build code faster is amazing. Using AI to build secure code faster is even better. But it requires you to stay in the driver’s seat.

Your Action Plan

Week 1: Audit

npm audit  # Or equivalent for your stack

Week 2: Fix Use AI to help fix issues, but verify fixes work.

Week 3: Automate Set up Dependabot or security monitoring.

Week 4: Document Create security checklist for future AI projects.

Total time: 2-3 hours ROI: Avoid security incidents, build secure-by-default

Resources

Security Scanners:

npm audit (built-in)
Snyk: https://snyk.io
OWASP Dependency-Check

Learning:

OWASP Top 10: https://owasp.org/www-project-top-ten/
Node.js Security: https://nodejs.org/en/docs/guides/security/

Tools:

Dependabot (GitHub)
CodeQL (advanced scanning)
npm-check-updates

Final Thought

I’m not saying don’t trust AI. I’m saying trust and verify.

AI helped me build a blog in record time. ✅ AI helped me fix security issues in minutes. ✅ But I had to run the audit that found them. ✅

The future of secure development isn’t human OR AI. It’s human AND AI, working as partners.

You bring: Judgment, context, security awareness, risk evaluation AI brings: Speed, knowledge, pattern recognition, rapid fixes

Together? Fast AND secure development.

P.S. The 7 vulnerabilities I found? Fixed in 10 minutes, zero cost, production-ready.

That’s the power of AI-assisted development with security-conscious oversight.

Now go audit your projects. You might be surprised what you find.

And when you do? Use AI to help fix it. Just verify the fix works. 😉

I Found 7 Security Flaws in My AI-Generated Blog

The Wake-Up Call

What Went Wrong?

The Problem: Unmaintained Package

Why AI Didn’t Catch It

The Hidden Danger: Transitive Dependencies

How I Used AI to Fix It (10 Minutes)

The Conversation

The “Trust But Verify” Framework

Phase 1: Development (Trust AI)

Phase 2: Validation (Verify Everything)

Phase 3: Remediation (Use AI to Fix)

My Security Checklist

Common AI Security Blind Spots

1. AI Doesn’t Know About Zero-Day Vulnerabilities

2. AI Optimizes for “Working Code”

3. AI Doesn’t Monitor Your Production Code

4. AI Can’t Judge Risk Context

Questions to Ask AI About Security

The Paradox: AI Made Me More Security-Conscious

My Evolved Workflow

Practical Takeaways

For Developers Using AI:

For Teams Adopting AI:

Tools I Use

NPM Projects

Automation

The Bottom Line

Your Action Plan

Resources

Final Thought