How to Maintain CyberSecurity When Coding with Python

One of the most commonly used open-source programming languages is Python. It is used in thousands of applications spanning over various levels of complexity and importance. Like any other programming language, cybersecurity is one of the main concerns when programming with this language.

As coding is the very basis of any application, it is at this stage where you can make sure that the resource being created is secure and can stand up to any cyber attack. This Python security cheat sheet, for example, is a good place to start coding securely using python. Other than that, you can take the following measures to develop secure python based apps.

Keep Up to Date

This is a general principle of development to always use up-to-date code in all projects. This is the first step you can take to ensure that your software product will work without issues and will not leave any door open for attackers.

Anyone working on Python knows that there are a lot of security improvements in version 3 of the language compared to version 2, and the latest release will make your software inherently more secure.

Keeping up-to-date transcends just the language version. One of the advantages of using Python is its rich community backing it. Anyone working on this language should watch what the community is saying. People find vulnerabilities and share them with fellow developers. Remaining in touch with the community will make sure that you are aware of any such issue and can take measures to secure your product against it. Conversely, if you don’t know about it, hackers do, and they’ll take advantage.

It must be noted that Python 2.x codes are not compatible with 3.x. You’ll have to rewrite a large portion of your code. Still, the following security improvements in the latest versions make it worth the effort:-

  • Strings are Unicode encoded by default.
  • Error handling and exception syntax are greatly improved.
  • The popular XML libraries are better configured with improved behavior.
  • Improved input and evaluation functions.

Use Shared Resources With Caution

As A large and rich community backs python, there are a lot of libraries that you can get to extend the features. However, validating the security situation of a package you get from the Python Package Index (PyPI) is not easy. Even though PyPI facilitates the package maintainers by giving them the option of signing the package to validate the integrity of the download and the identity of the author, it is worth noting that these packages don’t undergo a security review. Only use libraries from authors with a good reputation.

The case of the code you get from others is also the same. Any open-source code, including your software’s direct and transitive dependencies, needs to be from a trustworthy source. You can always use a Software Composition analysis tool to validate the integrity of open source code components.

Craft Error Messages Carefully

Disclosing too much information to the user in the form of an error message is another of the worst things you can do when writing a code. For example, if a user enters a wrong password and a valid username, you can display two error messages:-

  • The password is wrong – which will tell them they have the right username
  • The username or password is wrong – they will not be sure if the username is correct.

That’s just a simple example. There can be a lot of scenarios where an error message can help a user with malicious intent know the inner workings of your code which they can exploit to their advantage. As a rule, include the least possible information in the error messages.

Don’t Hard Code the Secrets

Many Python developers make common mistakes while writing code that is hardcoding sensitive information such as URLs with authentication parameters, passwords, and API keys. This is often done to make testing easier, but the real problem happens when the developers forget to fix this when finalizing the build.

Such passwords and other information can easily leak. The best practice to avoid such a thing is to never enter sensitive information as plain text and always encrypt it even if it is a testing build.

Use Segmentation

Another Python security best practice that you should never overlook is the use of virtual environments. It helps with security and keeps the development environment organized. The more organized you are, the easier it is to find and fix vulnerabilities.

Imagine an operating system that has no folder structure. All files, including configuration files, all the libraries they are using, text documents, images, music files, and videos, will be stored in one directory. The file names are the only way of organization and identification. This will make finding the correct file nearly impossible, and such chaos is a fertile ground for security vulnerabilities to grow.

Using segmentation by setting up a virtual environment makes sure that all the packages you need for your projects are neatly organized and isolated from other projects on your system. So, if a security issue does happen in one of your projects, it will not penetrate into the rest of your work.


Maintaining cybersecurity when developing software with python is one of the key things you can do to improve your organization’s cybersecurity. Always use the latest version of python, no matter if it needs you to rewrite a lot of your code because it has a lot of security improvements., Be careful when using shared libraries and dependencies; use SCA tools to validate them. Display error messages with just the right information so that nothing can be exploited. Avoid hardcoding secrets, and use virtualization and segmentation to isolate your projects from one another so that any threat cannot penetrate all of them.

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