Python, the most widely used language for application development, is a high-level language, but it isn't immune to external threats if certain coding practices aren't followed. Python comes with a wide range of libraries and frameworks. It's precisely for this reason that the Python language is fast at computations and doesn't lag behind, has become platform-independent, and is therefore more likely to be the cause of security breaches when a program is weak.
Here are 10 best practices for Python security programming that developers should follow to minimize vulnerabilities.
Update your Python version more often
In general, developers are advised not to put off updating software versions. Python is no exception to this rule. For example, when comparing Python versions 2 and 3, the latter has advanced security features that can help keep software secure.
Be careful when sharing
When a developer retrieves a project from the Python Package Index (PyPI), a community resource, there's a chance it contains vulnerabilities and bugs. PyPI gives package maintainers the option to sign their materials so users can verify the integrity of a download. However, the fact that packages on PyPI aren't security-reviewed should be cause for concern.
Make sure inputs are sanitized
In interactive software, user actions can cause the software to behave in a certain way. Sometimes seemingly useful input can become dangerous, potentially leading to injection attacks. SQL attacks are the most common attacks that infect software, turning an authorization check into administrative access to a web portal.
Use prepared statements
Databases that support prepared statements can protect software from external threats such as SQL injection, and with databases such as MySQL and MS SQL, they can improve performance when SQL prepared statements are repeated. In Python, these statements can be used even if it doesn't support them. Using prepared queries helps distinguish user-provided data from SQL queries, thereby preventing unauthorized changes to SQL queries.
Switch to a virtual environment for Python programming
Moving to virtual programming has benefits beyond keeping your software secure. Python programming requires access to many files, including configuration files, libraries, text documents, images, and music files, which can become quite cumbersome to organize when working offline. In a virtual environment, projects can be isolated from one another so that access to one doesn't affect another or its project.
Don't share your secrets
The hard-and-fast rule here is not to let the Internet store any important information that you code into your file, intentionally or unintentionally. Developers typically code passwords and URLs with authentication to make testing easier. But at some point later on, they could fall into the hands of attackers, giving them access to the code.
Let users see only what they need to
A lot of debugging information is generated throughout the trial-and-error cycle, which is quite harmful to the production cycle. So it's very important to separate the development cycle from the production cycle to prevent critical debugging information from falling into the wrong hands.
Bandit scans best
Use the Bandit package for every Python project to get reports in the form of an abstract syntax tree. It's best for scanning for vulnerabilities, such as common YAML issues, and ranking security risks from low to high while highlighting problematic lines of code.
Update your server
Often it's the server, not the code, that needs updating. In addition to checking for an updated software version—that is, including its compatibility with your Python code—make sure you have an up-to-date security management system.
