Common Python Applications That Are Vulnerable to Security Threats

Python is a language that has experienced explosive growth since its release and is now used extensively across industries by developers with different experience levels. One of its interesting points is that it is relatively accessible while at the same time providing enough features to keep veteran developers interested and motivated to continue working with it. Let’s take a look at some relevant examples of Python applications.

To go along with the official announcement of Extended Lifecycle Support for Python, we looked at some widely used Python applications. These are examples of huge codebases that security vulnerabilities would highly impact if running on top of Python 2 were supported by the application. On the other hand, it takes countless development hours to support the migration to Python 3.

Starting with web development and frameworks, Django is widely used as a foundation for high-performance, high-traffic websites. It ran on top of Python 2  until version 1.11, and all versions since have supported Python 3. If your website was developed with one of the first Django versions, you should consider updating all the code on your website – or extending your Python 2 support to remain secure.

Flask is another Python framework for web applications. As of Flask version 2.0.0, Python 2 is no longer supported, and only Python 3 receives official support from the Flask community. However, existing pre-Flask 2.0.0 applications would continue to run happily, given enough security assurances.

Another area with a high prevalence of Python applications, and libraries, is Artificial Intelligence and Machine Learning. Multiple high-profile libraries, like Tensorflow, Pandas, or Scikit-learn, are either fully implemented in Python or provide Python bindings for their internal functions. They are building blocks that can be added to other applications and provide access to state-of-the-art processing capabilities.

The data scientists will recognize some of those names, as they play into the visualization of data space as well, with graph plotting and information visualization. The availability of such libraries is one of the fundamental reasons why Python is so common among scientists and Ph.D. students worldwide. Even non-technical students will learn just enough Python to improve their projects.

The DevOps engineers will recognize Bazaar, Mercurial, Buildbot, and SCons as familiar names, and all are built on Python or tightly integrated with it. 

On the Enterprise, Tryton and Odoo are ERP and CRM platforms with large deployments.

These are code-heavy applications where any change, however minute, disrupts regular-business operations and thus is avoided until no other option is available. Users have dealt with the transition from Python 2 to Python 3 in some way or another, but that transition was not always peaceful for the end-users. 

Suppose your deployment is running just as you need it to run. You shouldn’t be forced into migration that will likely introduce unexpected issues like incompatible third-party applications and deprecated functionality. You should consider extending the secure lifecycle of your existing applications with Extended Lifecycle Support for Python.