Ever trusted your AI pair programmer a little too much? Last week, I decided to audit six months of code I’d written with AI assistance. What I found made my stomach drop—five glaring security vulnerabilities hiding in plain sight.

The scariest part? These weren’t edge cases or exotic attack vectors. They were fundamental security flaws that any junior developer should catch, yet somehow slipped past both me and my AI coding companion.

Here’s what I learned from my security wake-up call, and more importantly, how you can avoid making the same mistakes.

The Audit That Changed My Perspective

I’d been feeling pretty confident about my AI-assisted development workflow. Claude and I had been cranking out features, and everything seemed to work beautifully. But after reading about supply chain attacks targeting AI-generated code, paranoia got the better of me.

I spent three days going through every piece of code where I’d leaned heavily on AI assistance. The results were humbling. Out of 47 files, I found 5 serious vulnerabilities that could have been exploited in production.

Vulnerability #1: The Trusting Database Query

The first vulnerability I found was a classic SQL injection waiting to happen. I’d asked my AI to help build a user search feature, and it delivered this gem:

def search_users(query):
    cursor.execute(f"SELECT * FROM users WHERE name LIKE '%{query}%'")
    return cursor.fetchall()

I remember being focused on getting the search functionality working quickly. The AI generated clean, readable code that passed my manual tests. But look at that f-string—it’s directly interpolating user input into a SQL query.

An attacker could easily inject '; DROP TABLE users; -- and wreak havoc on the database. The fix is straightforward with parameterized queries:

def search_users(query):
    cursor.execute("SELECT * FROM users WHERE name LIKE %s", (f'%{query}%',))
    return cursor.fetchall()

Prevention strategy: Always audit database interactions in AI-generated code. Make parameterized queries a non-negotiable requirement in your prompts.

Vulnerability #2: The Oversharing API

The second vulnerability was more subtle. I’d asked for help building a user profile API endpoint, and the AI helpfully returned all user data:

app.get('/api/user/:id', async (req, res) => {
  const user = await User.findById(req.params.id);
  res.json(user);
});

This looks innocent until you realize it’s exposing sensitive fields like password hashes, email verification tokens, and internal user flags to anyone who can guess a user ID.

The AI wasn’t wrong—it gave me exactly what I asked for. But it couldn’t read my mind about what data should remain private. The fix required explicit field selection:

app.get('/api/user/:id', async (req, res) => {
  const user = await User.findById(req.params.id)
    .select('name email avatar createdAt');
  res.json(user);
});

Prevention strategy: Be explicit about data privacy in your prompts. Ask AI to “return only public user fields” rather than just “return user data.”

Vulnerability #3: The Missing Bouncer

The third vulnerability was an authorization bypass. I’d built an admin dashboard with AI assistance, and while we properly implemented authentication (checking if a user was logged in), we completely missed authorization (checking if they were actually an admin):

@app.route('/admin/delete-user/<user_id>')
@login_required
def delete_user(user_id):
    User.query.filter_by(id=user_id).delete()
    db.session.commit()
    return jsonify({"message": "User deleted"})

Any logged-in user could delete any other user by hitting this endpoint directly. The AI had focused on the database operation I’d requested without considering the security context.

Adding proper role checking fixed the issue:

@app.route('/admin/delete-user/<user_id>')
@login_required
@admin_required
def delete_user(user_id):
    User.query.filter_by(id=user_id).delete()
    db.session.commit()
    return jsonify({"message": "User deleted"})

Prevention strategy: Always specify authorization requirements in your prompts. Don’t assume AI will infer security boundaries from context.

Vulnerability #4: The Predictable Token

For a password reset feature, the AI generated this token creation logic:

import random
import string

def generate_reset_token():
    return ''.join(random.choices(string.ascii_letters + string.digits, k=20))

The code looks professional and works perfectly for generating random-looking tokens. The problem? Python’s random module uses a predictable pseudorandom number generator that can be exploited by attackers who can guess the seed.

For security-sensitive tokens, cryptographically secure randomness is essential:

import secrets
import string

def generate_reset_token():
    return ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(32))

Prevention strategy: When asking AI to generate tokens, keys, or any security-sensitive random data, explicitly request “cryptographically secure” methods.

Vulnerability #5: The Trusting File Handler

The final vulnerability was in a file upload feature. The AI created a clean upload handler that saved files with their original names:

@app.route('/upload', methods=['POST'])
def upload_file():
    file = request.files['file']
    file.save(f'./uploads/{file.filename}')
    return jsonify({"message": "File uploaded successfully"})

This opens the door to path traversal attacks. An attacker could upload a file named ../../../etc/passwd and potentially overwrite critical system files.

The fix involves sanitizing filenames and adding proper validation:

import os
from werkzeug.utils import secure_filename

@app.route('/upload', methods=['POST'])
def upload_file():
    file = request.files['file']
    filename = secure_filename(file.filename)
    file.save(os.path.join('./uploads', filename))
    return jsonify({"message": "File uploaded successfully"})

Prevention strategy: When requesting file handling code, specify security requirements upfront: “secure file upload with path traversal protection.”

Building Better Security Habits

This audit taught me that AI coding tools are incredibly powerful, but they’re not security experts. They generate code based on patterns in their training data, which unfortunately includes plenty of insecure examples.

The solution isn’t to abandon AI assistance—it’s to build better security habits into our AI-powered workflows. Start treating security as a collaboration between you and your AI tool, not something you can outsource entirely.

Your next step? Pick a recent project where you relied heavily on AI assistance and run your own security audit. Focus on authentication, authorization, input validation, and data exposure. You might be surprised by what you find, but catching vulnerabilities in development is infinitely better than discovering them in production.