Authentication and Authorization Defects in AI-Generated Code

The Systemic Nature of Auth Vulnerabilities

A 2025 study by Databricks revealed:

"AI-generated authentication systems frequently incorporate outdated patterns and fail to implement modern security practices, creating what we call 'authentication debt' in codebases."

The Statistics

The research found that:

• 73% of AI-generated authentication code lacked proper session management
• 81% stored passwords insecurely (MD5, SHA1, or plaintext)

These aren't edge cases—they're the norm in AI-generated authentication code.

1.2.1 Insecure Password Storage

The Problem

The most alarming finding from multiple studies is the prevalence of plaintext or weakly hashed password storage in AI-generated code.

As noted by Infisical's security team:

"AI models trained on older codebases often suggest MD5 or SHA1 for password hashing, algorithms that have been cryptographically broken for over a decade."

AI-Generated Vulnerable Code

# Prompt: "Implement user registration with password"
import hashlib
import mysql.connector

def register_user(username, password, email):
    conn = mysql.connector.connect(host='localhost', database='app')
    cursor = conn.cursor()

    # ❌ VULNERABLE: MD5 is cryptographically broken
    password_hash = hashlib.md5(password.encode()).hexdigest()

    # ❌ VULNERABLE: No salt means identical passwords have identical hashes
    query = "INSERT INTO users (username, password, email) VALUES (%s, %s, %s)"
    cursor.execute(query, (username, password_hash, email))

    conn.commit()
    return {"status": "success", "user_id": cursor.lastrowid}

# Even worse: Some AI models generate this
def register_user_worse(username, password, email):
    # ❌ CRITICAL: Storing plaintext passwords
    user_data = {
        "username": username,
        "password": password,  # Never do this!
        "email": email
    }
    database.save(user_data)

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