Python Code Security Cyber Range

This cyber range helps you develop your knowledge in finding and remediating vulnerabilities in Python code. You’ll build and reinforce your skills as you progress through labs covering a wide range of Python code security topics, including using Bandit, PyT and other tools to find common security issues in Python code, perform taint and control flow analysis, and recognize vulnerabilities that can lead to common application attacks, such as cross-site scripting or XPath injection. You will also practice identifying supply chain vulnerabilities, unsafe deserialization and other risks.


Introduction to Python Bandit

Lab — 00:13:00

Python bandit is one of the most commonly used Python linters and static analysis tools. This lab introduces the use of bandit for analysis of Python code.

Introduction to Python Control Flow Analysis

Lab — 00:11:00

Control flow analysis is useful for identifying mistakes or unexpected flows in a program. This lab demonstrates the use of staticfg for control flow analysis in Python.

Command Injection in Python

Lab — 00:13:00

A command injection vulnerability enables an attacker to run malicious code on the system where an application is running. This lab demonstrates how to identify, exploit and mitigate these vulnerabilities in Python.

XPath Injection in Python

Lab — 00:15:00

XPath is a programming language for extracting data from XML files; however, it is vulnerable to injection attacks. This lab demonstrates vulnerable code and how to exploit and remediate it.

XML Attacks in Python

Lab — 00:12:00

XML is a useful method for storing structured data, but code parsing it can be vulnerable to a number of attacks. This lab demonstrates some of the ways that different XML parsing functions can be exploited in Python.

Race Conditions in Python

Lab — 00:14:00

Race condition vulnerabilities exist when the proper operation of a program depends upon operations being run sequentially back-to-back. This lab demonstrates how to identify, exploit and remediate these vulnerabilities.

Cross-Site Scripting in Python

Lab — 00:16:00

Cross-site-scripting (XSS) is one of the most common web application vulnerabilities in existence. This lab demonstrates how to identify, exploit and remediate XSS vulnerabilities in Flask web applications.

Cross-Site Request Forgery in Python

Lab — 00:11:00

Cross-site request forgery (CSRF) vulnerabilities allow an attacker to force unauthorized actions to be taken on an authenticated user’s account. This lab demonstrates how to identify, exploit and remediate these vulnerabilities in Python code.

Supply Chain Vulnerabilities in Python

Lab — 00:12:00

Very few applications are designed to be completely standalone, and a program’s dependencies can introduce vulnerabilities. This lab demonstrates the use of tools for identifying potential supply chain vulnerabilities in Python programs.

Unsafe Deserialization in Python

Lab — 00:14:00

Serialization is helpful for making Python objects and data writable to files and transferrable over the network. This lab demonstrates how some serialization options can be vulnerable to exploitation in Python and how to securely serialize data.

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All Infosec training maps directly to the NICE Workforce Framework for Cybersecurity to guide you from beginner to expert across 52 Work Roles.

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