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Security oriented static analyser for python code.

Project description

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A security linter from PyCQA


Bandit is a tool designed to find common security issues in Python code. To do this Bandit processes each file, builds an AST from it, and runs appropriate plugins against the AST nodes. Once Bandit has finished scanning all the files it generates a report.

Bandit was originally developed within the OpenStack Security Project and later rehomed to PyCQA.


Bandit is distributed on PyPI. The best way to install it is with pip:

Create a virtual environment (optional):

virtualenv bandit-env
python3 -m venv bandit-env
# And activate it:
source bandit-env/bin/activate

Install Bandit:

pip install bandit
pip3 install bandit

Run Bandit:

bandit -r path/to/your/code

Bandit can also be installed from source. To do so, download the source tarball from PyPI, then install it:

python install


Example usage across a code tree:

bandit -r ~/your_repos/project

Example usage across the examples/ directory, showing three lines of context and only reporting on the high-severity issues:

bandit examples/*.py -n 3 -lll

Bandit can be run with profiles. To run Bandit against the examples directory using only the plugins listed in the ShellInjection profile:

bandit examples/*.py -p ShellInjection

Bandit also supports passing lines of code to scan using standard input. To run Bandit with standard input:

cat examples/ | bandit -

For more usage information:

bandit -h


Bandit allows specifying the path of a baseline report to compare against using the base line argument (i.e. -b BASELINE or --baseline BASELINE).

bandit -b BASELINE

This is useful for ignoring known vulnerabilities that you believe are non-issues (e.g. a cleartext password in a unit test). To generate a baseline report simply run Bandit with the output format set to json (only JSON-formatted files are accepted as a baseline) and output file path specified:

bandit -f json -o PATH_TO_OUTPUT_FILE

Version control integration

Use pre-commit. Once you have it installed, add this to the .pre-commit-config.yaml in your repository (be sure to update rev to point to a real git tag/revision!):

-   repo:
    rev: '' # Update me!
    - id: bandit

Then run pre-commit install and you’re ready to go.


An optional config file may be supplied and may include:
  • lists of tests which should or shouldn’t be run

  • exclude_dirs - sections of the path, that if matched, will be excluded from scanning (glob patterns supported)

  • overridden plugin settings - may provide different settings for some plugins

Per Project Command Line Args

Projects may include a .bandit file that specifies command line arguments that should be supplied for that project. The currently supported arguments are:

  • targets: comma separated list of target dirs/files to run bandit on

  • exclude: comma separated list of excluded paths

  • skips: comma separated list of tests to skip

  • tests: comma separated list of tests to run

To use this, put a .bandit file in your project’s directory. For example:

exclude: /test
tests: B101,B102,B301


In the event that a line of code triggers a Bandit issue, but that the line has been reviewed and the issue is a false positive or acceptable for some other reason, the line can be marked with a # nosec and any results associated with it will not be reported.

For example, although this line may cause Bandit to report a potential security issue, it will not be reported:

self.process = subprocess.Popen('/bin/echo', shell=True)  # nosec

Vulnerability Tests

Vulnerability tests or “plugins” are defined in files in the plugins directory.

Tests are written in Python and are autodiscovered from the plugins directory. Each test can examine one or more type of Python statements. Tests are marked with the types of Python statements they examine (for example: function call, string, import, etc).

Tests are executed by the BanditNodeVisitor object as it visits each node in the AST.

Test results are managed in the Manager and aggregated for output at the completion of a test run through the method output_result from Manager instance.

Writing Tests

To write a test:
  • Identify a vulnerability to build a test for, and create a new file in examples/ that contains one or more cases of that vulnerability.

  • Consider the vulnerability you’re testing for, mark the function with one or more of the appropriate decorators: - @checks(‘Call’) - @checks(‘Import’, ‘ImportFrom’) - @checks(‘Str’)

  • Create a new Python source file to contain your test, you can reference existing tests for examples.

  • The function that you create should take a parameter “context” which is an instance of the context class you can query for information about the current element being examined. You can also get the raw AST node for more advanced use cases. Please see the file for more.

  • Extend your Bandit configuration file as needed to support your new test.

  • Execute Bandit against the test file you defined in examples/ and ensure that it detects the vulnerability. Consider variations on how this vulnerability might present itself and extend the example file and the test function accordingly.

Extending Bandit

Bandit allows users to write and register extensions for checks and formatters. Bandit will load plugins from two entry-points:

  • bandit.formatters

  • bandit.plugins

Formatters need to accept 5 things:

  • manager: an instance of bandit manager

  • fileobj: the output file object, which may be sys.stdout

  • sev_level : Filtering severity level

  • conf_level: Filtering confidence level

  • lines=-1: number of lines to report

Plugins tend to take advantage of the bandit.checks decorator which allows the author to register a check for a particular type of AST node. For example

def prohibit_unsafe_deserialization(context):
    if 'unsafe_load' in context.call_function_name_qual:
        return bandit.Issue(
            text="Unsafe deserialization detected."

To register your plugin, you have two options:

  1. If you’re using setuptools directly, add something like the following to your setup call:

    # If you have an imaginary bson formatter in the bandit_bson module
    # and a function called `formatter`.
    entry_points={'bandit.formatters': ['bson = bandit_bson:formatter']}
    # Or a check for using mako templates in bandit_mako that
    entry_points={'bandit.plugins': ['mako = bandit_mako']}
  2. If you’re using pbr, add something like the following to your setup.cfg file:

    bandit.formatters =
        bson = bandit_bson:formatter
    bandit.plugins =
        mako = bandit_mako


Follow our Contributing file:

Reporting Bugs

Bugs should be reported on github. To file a bug against Bandit, visit:

Show Your Style

Security Status

Use our badge in your project’s README!

using Markdown:

[![security: bandit](](

using RST:

.. image::
    :alt: Security Status

Under Which Version of Python Should I Install Bandit?

The answer to this question depends on the project(s) you will be running Bandit against. If your project is only compatible with Python 3.5, you should install Bandit to run under Python 3.5. If your project is only compatible with Python 3.8, then use 3.8 respectively. If your project supports both, you could run Bandit with both versions but you don’t have to.

Bandit uses the ast module from Python’s standard library in order to analyze your Python code. The ast module is only able to parse Python code that is valid in the version of the interpreter from which it is imported. In other words, if you try to use Python 2.7’s ast module to parse code written for 3.5 that uses, for example, yield from with asyncio, then you’ll have syntax errors that will prevent Bandit from working properly. Alternatively, if you are relying on 2.7’s octal notation of 0777 then you’ll have a syntax error if you run Bandit on 3.x.


Bandit docs:

Python AST module documentation:

Green Tree Snakes - the missing Python AST docs:

Documentation of the various types of AST nodes that Bandit currently covers or could be extended to cover:

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