Getting Started with Bandit

One of the many benefits of using and working with python is its ability to introspect itself. This empowers us to write and use tools to analyze the projects we use and write. Tools written in Python can use the built-in ast module to parse and analyze other Python code into an " A bstract S yntax T ree". Perhaps you've heard of Flake8, PyFlakes, PyLint, Radon, or another tool that provides style checking, lint discovery, or complexity computation? They all use the AST to provide that functionality. There's also a tool called Bandit that uses the AST to provide static security analysis of Python programs.

Starting out with Bandit is simple

$ pip install bandit $ bandit path/to/code/to/check/*.py

Bandit scans our files for any known vulnerabilities and then provides us with explicit feedback about what it found, the severity of the problem, and how confident it is in its discovery. Let's take a look at an example. Bandit knows about PyYAML and some of its past security vulnerabilities, so let's give it example some code that I wrote:

# blog_ex.py import yaml def to_yaml(object): return yaml.dump(object) def from_yaml(yaml_str): return yaml.load(yaml_str) yaml_str = to_yaml({ # Yes, this is some metadata about this blog ;) 'layout': 'post', 'title': 'Getting Started with Bandit', 'date': '2017-01-16 10:00', 'author': 'Ian Cordasco', }) parsed_yaml = from_yaml(yaml_str)

Running Bandit on this file results in:

~/o/bandit bandit blog_ex.py [main] INFO profile include tests: None [main] INFO profile exclude tests: None [main] INFO cli include tests: None [main] INFO cli exclude tests: None [main] INFO running on Python 2.7.12 [node_visitor] INFO Unable to find qualified name for module: blog_ex.py Run started:2017-01-11 20:47:39.901651 Test results: >> Issue: [B506:yaml_load] Use of unsafe yaml load. Allows instantiation of arbitrary objects. Consider yaml.safe_load(). Severity: Medium Confidence: High Location: blog_ex.py:8 7 def from_yaml(yaml_str): 8 return yaml.load(yaml_str) 9 -------------------------------------------------- Code scanned: Total lines of code: 12 Total lines skipped (#nosec): 0 Run metrics: Total issues (by severity): Undefined: 0 Low: 0 Medium: 1 High: 0 Total issues (by confidence): Undefined: 0 Low: 0 Medium: 0 High: 1 Files skipped (0):

Let's look specifically at the Test results section. We see here that there's an issue labeled B506 and named yaml_load . The message then tells us what the specific issue is and a potential way to fix it:

Use of unsafe yaml load. Allows instantiation of arbitrary objects. Consider yaml.safe_load().

After that message, we are given information about:

How severe the issue is - Medium in this case How confident Bandit is that there's a problem - High Where the issue is - in blog_ex.py on line number 8 And the code in question, complete with line numbers.

If we were certain we wanted to disable this check, we could then "skip" it by its ID ( B506 ) explicitly:

~/o/bandit bandit -s B506 blog_ex.py [main] INFO profile include tests: None [main] INFO profile exclude tests: None [main] INFO cli include tests: None [main] INFO cli exclude tests: B506 [main] INFO running on Python 2.7.12 [node_visitor] INFO Unable to find qualified name for module: blog_ex.py Run started:2017-01-11 20:55:05.987581 Test results: No issues identified. Code scanned: Total lines of code: 12 Total lines skipped (#nosec): 0 Run metrics: Total issues (by severity): Undefined: 0 Low: 0 Medium: 0 High: 0 Total issues (by confidence): Undefined: 0 Low: 0 Medium: 0 High: 0 Files skipped (0):

Further, we're able to store this configuration in a file. If we're using tox for our project, then we can add the following to our tox.ini file:

# tox.ini [bandit] skips = B506

And run

~/o/bandit bandit --ini tox.ini blog_ex.py [main] INFO Using .bandit arg for skipped tests [main] INFO profile include tests: None [main] INFO profile exclude tests: None [main] INFO cli include tests: None [main] INFO cli exclude tests: B506 [main] INFO running on Python 2.7.12 [node_visitor] INFO Unable to find qualified name for module: blog_ex.py Run started:2017-01-11 20:59:08.793653 Test results: No issues identified. Code scanned: Total lines of code: 12 Total lines skipped (#nosec): 0 Run metrics: Total issues (by severity): Undefined: 0 Low: 0 Medium: 0 High: 0 Total issues (by confidence): Undefined: 0 Low: 0 Medium: 0 High: 0 Files skipped (0):

Finally, if our first run of Bandit is giving us a lot of noise, we can filter by severity and confidence . It's very reasonable for us to start addressing only the issues that are the highest severity and the highest confidence. To see only those, we can run:

$ bandit -lll -iii /path/to/code/*.py

Providing -l increases the minimum severity level and it can be repeated without having to specify it individually. Likewise, -i increases the minimum confidence level. Specifying each of them three times means we will only see the issues with HIGH severity and confidence.

Finally, it is important to note that Bandit should be installed and run with the version of Python we're writing for. If our code is using features in Python 3 only, Bandit should be installed on Python 3 and run from Python 3, otherwise it may not be able to fully detect problems due to not being able to parse the files.