In this tutorial, I guide you through using Bandit to detect security vulnerabilities in a Python application. Bandit is a powerful static analysis tool capable of identifying issues such as SQL injections, hardcoded passwords, and cryptography problems, while also promoting good security practices. I start by creating a directory for our project and setting up a virtual environment on Windows 11, which, although optional, is a practice I recommend for managing dependencies separately from the global Python installation.

Let’s secure your Python apps

After activating the virtual environment and installing Bandit, I explore its capabilities and various options that enhance its scanning effectiveness. To demonstrate Bandit’s utility, I choose DjanGoat, a deliberately vulnerable Django application available on GitHub, as our target project. This application, designed with numerous vulnerabilities, serves as an excellent resource for understanding how Bandit works.

Once DjanGoat is cloned to our local environment, I run Bandit to perform a recursive search through the application’s directories, quickly identifying security flaws. The output categorizes issues by severity and confidence, highlighting areas requiring immediate attention, such as potential SQL injections and hardcoded passwords.

I further enhance the tutorial by demonstrating how to capture Bandit’s output in a CSV file for easy review and record-keeping. This step is crucial for auditing and fixing identified vulnerabilities efficiently. Lastly, I integrate Bandit into a CI/CD pipeline using a simple batch file, illustrating how Bandit can automatically break builds when it detects security issues, thereby ensuring that security considerations are an integral part of the development process.