Bandit is an open-source security-oriented static analyser for Python code, designed to find common security issues early in the development lifecycle. It processes each file, builds an Abstract Syntax Tree (AST) from it, and runs a set of security-focused plugins against the AST nodes, generating reports with severity and confidence levels. Maintained by the PyCQA community, Bandit is currently at version 1.9.4 and requires Python >=3.10. Its release cadence focuses on compatibility updates and rule maintenance, indicating a stable and actively supported utility.
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Bandit is primarily a command-line tool. To quickly scan your code for security issues, you first create a Python file, and then run Bandit against it. This example creates a dummy file with common vulnerabilities and instructs on how to run Bandit.
Avoid `shell=True`. Instead, pass commands and arguments as a list (e.g., `subprocess.call(['ls', '-l'])`). If `shell=True` is unavoidable, ensure all user-supplied input is rigorously sanitized.
Replace `assert` statements used for critical logic with proper exception handling (e.g., `raise ValueError(...)` or `raise AssertionError(...)`).
Review each reported issue carefully. Use inline comments like `# nosec` to suppress specific findings that are confirmed false positives or acceptable risks, documenting the reason for suppression.
Integrate Bandit into your CI/CD pipeline for comprehensive scans on pull requests or merges. For local development, consider running it less frequently, targeting specific files, or configuring it to only fail on high-severity issues.
For YAML or TOML configurations, always run Bandit with `bandit -c your_config.yaml -r .` or `bandit -c pyproject.toml -r .`. Ensure the configuration file path is correct.
Install Bandit using pip: `pip install bandit`
Ensure Bandit is installed (`pip install bandit`) and verify that the directory containing the `bandit` executable (e.g., `~/.local/bin` or a virtual environment's `bin` directory) is in your system's PATH. You might need to reactivate your virtual environment or restart your terminal.
Install the `pbr` package: `pip install pbr`. Alternatively, update Bandit to a more recent version where this dependency issue might be resolved or managed differently: `pip install --upgrade bandit`.
Specify the severity or confidence level using the correct flag and format, typically in lowercase and without the level name directly as an argument, e.g., `bandit examples/*.py --severity-level high` or `bandit examples/*.py --confidence-level medium`.
Ensure that the Python version used to run Bandit is compatible with the Python code being analyzed. If your project uses Python 3.10+, run Bandit with a Python 3.10+ interpreter. For example, if you are scanning a Python 3.7 project, you should run Bandit with a Python 3.7 interpreter. Consider using a virtual environment to manage Python versions. Update Bandit to the latest version if there are compatibility concerns: `pip install --upgrade bandit`.
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