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Coding is more than developing a program. What if the code you generate is vulnerable to thefts and unauthorized intrusions? Protecting sensitive data is as important as developing the application of choice, for data once stolen is as dangerous as malware. Python, the most adopted language for app development, though is a high-level language is not immune to external threats, unless certain coding practices are followed. Python comes with a wide array of libraries and frameworks, the very reason why Python language is fast at computing and off-late has become platform-agnostic and hence more are the chances that a weak python program is a reason for the security breaches. Here are the top 10 Python security programming practices, developers should practice ensuring the least vulnerability.
In general, it is suggested that developers should not delay updating the software versions. Python is no exception to this rule. For example, when Python 2 and 3 versions are compared, the latter has advanced security features that can keep the software secure.
When a developer pulls a project from the community resource Python Package Index (PyPI), there is a chance that it is carrying vulnerabilities and bugs. PyPI gives package maintainers the option of signing their submissions so that adopters can validate the download's integrity. However, the fact that packages in PyPI do not go through security review should raise a few red flags.
In the case of interactive software, user inputs might cause the software to behave in a certain way. The very useful inputs at times can turn dangerous, leading to possible injection attacks. SQL attacks are most commonly found attacks that infect software by turning an authorization check into administrative access to a web portal.
Databases that support using prepared statements can protect the software from external threats like SQL injection, and with databases like MySQL, and MS SQL, it can result in improved performances if the prepared SQL statements are repeated. In the case of Python, these statements can be used even if it doesn't support them. Using prepared queries helps differentiate between user-provided data and SQL queries and thereby preventing unauthorized modification of SQL queries.
Going virtual for programming has benefits beyond keeping your software safe. Python programming needs access to a variety of files including configuration files, libraries, text documents, images, and music files – whose organization can become rather cumbersome when you work offline. In a virtual environment, the projects can be kept isolated from one another so that access to one doesn't affect the other or the project.
The hard rule here is not to let the internet store any of the important information, you encode into your file, intentionally or unintentionally. Usually, developers encode the passwords, and URLs, with authentication for ease of testing. But at some later point in time, it can get into the hands of malicious actors giving way to the code.
Throughout the trial-and-error cycle, a lot of debugging information is generated, which is quite harmful to the production cycle. Therefore, it is highly important to separate the development cycle from the production cycle to prevent the critical debugging information from falling into the hands of the wrong players.
Using pycrypto for cryptography is passe. Ever since it was found vulnerable, and no security update is released to fix the problem, it has been suggested to abandon the tool. Instead, go for pip install pycryptodome for cryptography.
Use the Bandit package for each Python project to get reports in an abstract syntax tree. It is the best for scanning vulnerabilities such as common issues with YAML, ranking security risks from low to high highlighting the problem lines in the code.
Many times, it is the server and not the code which needs the update. Apart from checking for an updated version of the software, ie., including its compatibility with Python code, ensure an updated security management system is in place.
