Difference between revisions of "Bandit - Static Code Analyss for Python Code"

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This example is using the Bandit example files from their github (https://github.com/PyCQA/bandit). For this case we are using the imports.py Example with the following Source Code:
This example is using the Bandit example files from their github (https://github.com/PyCQA/bandit). For this case we are using the imports.py Example with the following Source Code:


  import sqlalchemy
  import os
  # bad
  import pickle
  query = "SELECT * FROM foo WHERE id = '%s'" % identifier
  import sys
  query = "INSERT INTO foo VALUES ('a', 'b', '%s')" % value
  import subprocess
query = "DELETE FROM foo WHERE id = '%s'" % identifier
query = "UPDATE foo SET value = 'b' WHERE id = '%s'" % identifier
query = """WITH cte AS (SELECT x FROM foo)
SELECT x FROM cte WHERE x = '%s'""" % identifier
# bad alternate forms
query = "SELECT * FROM foo WHERE id = '" + identifier + "'"
query = "SELECT * FROM foo WHERE id = '{}'".format(identifier)
# bad
cur.execute("SELECT * FROM foo WHERE id = '%s'" % identifier)
cur.execute("INSERT INTO foo VALUES ('a', 'b', '%s')" % value)
cur.execute("DELETE FROM foo WHERE id = '%s'" % identifier)
cur.execute("UPDATE foo SET value = 'b' WHERE id = '%s'" % identifier)
# bad alternate forms
cur.execute("SELECT * FROM foo WHERE id = '" + identifier + "'")
cur.execute("SELECT * FROM foo WHERE id = '{}'".format(identifier))
# good
cur.execute("SELECT * FROM foo WHERE id = '%s'", identifier)
cur.execute("INSERT INTO foo VALUES ('a', 'b', '%s')", value)
cur.execute("DELETE FROM foo WHERE id = '%s'", identifier)
cur.execute("UPDATE foo SET value = 'b' WHERE id = '%s'", identifier)
# bug: https://bugs.launchpad.net/bandit/+bug/1479625
def a():
    def b():
        pass
    return b
a()("SELECT %s FROM foo" % val)
# real world false positives
choices=[('server_list', _("Select from active instances"))]
print("delete from the cache as the first argument")


After executing bandit on the File
After executing bandit on the File
  bandit imports.py
  bandit imports.py
you get the following output
you get the following output:


[[File:2023-01-14 21 07 27-Window.png|700px|al]]
[[File:2023-01-14 21 07 27-Window.png|700px|al]]


Bandit has found two Low Severity Issues regarding Problems with the imported Packages. A detailed information is given for every Issue found. It also shows the CWE Number associated with it.
Bandit has found two Low Severity Issues regarding Problems with the imported Packages. A detailed information is given for every Issue found. It also shows the CWE Number associated with it.
== Results ==
Bandit categorizes possible Problems into three Risk Categories Low, Medium, High each combined with a Confidence Score of Low, Medium High. This shows the impact of the Issue and how confident Bandit is, that this is really an issue.
[[File:Low Risk.png|700px|al]]
[[File:Medium_Risk.png|700px|al]]
[[File:High_Risk.png|700px|al]]


== References ==
== References ==
Line 67: Line 48:
* https://pypi.org/project/bandit/#files
* https://pypi.org/project/bandit/#files
* https://bandit.readthedocs.io/en/latest/start.html
* https://bandit.readthedocs.io/en/latest/start.html
[[Category:Documentation]]
[[Category:Documentation]]

Latest revision as of 09:05, 31 January 2023

Summary

This documentation shows how to install and use Bandit, a static analysis tool for C/C++ source code. This Tool can be used to identify possible Security risks categorized in three different Severity Levels (Low, Medium, High).

Requirements

  • Python Version 3.7 or higher
  • Python Package: bandit

Installation and Usage

Bandit can be easily installed via the pip install command. Furthermore it can be found on PyPi for manual installation. If you want to install it manually please follow the attached References. 

pip install bandit

For executing the analysis just enter die File or Folder you want to be analyzed in a Python Terminal 

bandit <File/Folder>

Example

This example is using the Bandit example files from their github (https://github.com/PyCQA/bandit). For this case we are using the imports.py Example with the following Source Code: 

import os
import pickle
import sys
import subprocess

After executing bandit on the File 

bandit imports.py

you get the following output:

al

Bandit has found two Low Severity Issues regarding Problems with the imported Packages. A detailed information is given for every Issue found. It also shows the CWE Number associated with it.

Results

Bandit categorizes possible Problems into three Risk Categories Low, Medium, High each combined with a Confidence Score of Low, Medium High. This shows the impact of the Issue and how confident Bandit is, that this is really an issue.

al

al

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References

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