Anthropic warns Claude Mythos Preview finds bugs faster than developers can patch them

Introduction

In this tutorial, you'll learn how to use a basic AI-powered vulnerability scanning tool similar to what Anthropic's Claude Mythos Preview might use. While you won't be able to replicate the full power of Claude's AI, you'll understand how automated systems can identify security issues in code. This tutorial will teach you how to set up a simple vulnerability scanner using Python and common security tools.

Prerequisites

• Basic understanding of command-line interface (CLI)
• Python 3.7 or higher installed on your computer
• Basic knowledge of how to install Python packages using pip
• Access to a simple Python code file to scan (for demonstration purposes)

Why this matters: Understanding how AI tools like Claude can identify vulnerabilities helps developers improve security in their software. This knowledge is crucial as AI systems become more prevalent in cybersecurity.

Step-by-Step Instructions

1. Set Up Your Development Environment

First, we need to create a clean environment for our work. Open your terminal or command prompt and create a new folder for this project.

mkdir vulnerability_scanner
 cd vulnerability_scanner

Why: Creating a dedicated folder helps organize your work and prevents conflicts with other projects.

2. Install Required Python Packages

We'll use several Python packages to create our basic scanner. Run the following commands in your terminal:

pip install bandit
pip install safety
pip install flake8

Why: These tools help identify different types of security issues:

• Bandit: Finds common security issues in Python code
• Safety: Checks for vulnerable dependencies
• Flake8: Checks code style and potential errors

3. Create a Sample Python File to Scan

Create a simple Python file that contains some common security issues for demonstration:

touch sample_code.py

Now open the file in a text editor and add this code:

import os
import subprocess

# Vulnerable code example
password = "secret123"

# This is insecure - using eval
user_input = input("Enter command: ")
eval(user_input)

# Using os.system (insecure)
subprocess.run("ls -la", shell=True)

# Hardcoded credentials
print(f"Database password: {password}")

Why: This sample code has several security issues that our scanner will detect. It demonstrates how AI tools can find problems that developers might miss.

4. Run Bandit Security Scan

Bandit is a tool that finds common security issues in Python code:

bandit -r sample_code.py

This command will scan your file and output any security issues it finds. You should see warnings about:

• Use of eval() function
• Use of os.system() or subprocess with shell=True
• Hardcoded passwords

Why: Bandit helps identify security flaws that could be exploited by attackers.

5. Run Safety Dependency Check

First, create a requirements file:

touch requirements.txt

Add some dependencies to the file:

requests==2.20.0
flask==0.12.0

Then run the safety check:

safety check -r requirements.txt

Why: Safety checks for known vulnerable packages in your project dependencies, which is crucial for maintaining software security.

6. Run Flake8 Code Analysis

Flake8 checks code style and potential errors:

flake8 sample_code.py

This will show code style issues and potential errors in your Python file.

Why: Code quality tools like Flake8 help maintain clean, readable code that's less likely to contain security issues.

7. Combine All Scans in a Simple Script

Create a Python script that runs all scans automatically:

touch scan_all.py

Add this code to the file:

import subprocess
import sys

def run_bandit():
    result = subprocess.run(["bandit", "-r", "sample_code.py"], capture_output=True, text=True)
    print("Bandit Results:")
    print(result.stdout)

def run_safety():
    result = subprocess.run(["safety", "check", "-r", "requirements.txt"], capture_output=True, text=True)
    print("Safety Results:")
    print(result.stdout)

def run_flake8():
    result = subprocess.run(["flake8", "sample_code.py"], capture_output=True, text=True)
    print("Flake8 Results:")
    print(result.stdout)

if __name__ == "__main__":
    run_bandit()
    run_safety()
    run_flake8()

Run the script:

python scan_all.py

Why: This creates an automated workflow that mimics how AI tools might integrate multiple security checks into one process.

8. Analyze the Results

Review the output from all three tools. Each tool identifies different types of issues:

• Bandit finds code-level security issues
• Safety finds dependency-level vulnerabilities
• Flake8 finds code style and potential errors

Why: Understanding how different tools work together gives you insight into how AI systems like Claude can analyze code comprehensively.

Summary

In this tutorial, you've learned how to set up and use basic vulnerability scanning tools that work similarly to what Anthropic's Claude Mythos Preview might employ. You've created a simple scanner that combines:

• Bandit for finding security issues in Python code
• Safety for checking vulnerable dependencies
• Flake8 for code quality analysis

This demonstrates how automated tools can help identify problems faster than manual code reviews, which is exactly what the news article describes about Claude's capabilities. While this is a simplified version, it shows the foundation of how AI-powered vulnerability detection works in real-world applications.

Remember, the key takeaway is that as AI systems become more advanced, they can identify security issues faster than human developers, which creates both opportunities and challenges in cybersecurity.