Python Security Best Practices: A Comprehensive Guide for Engineers

We wanted to put together a high-level guide on Python security best practices to help every engineer get up to speed on the topic. Being one of the most popular programming languages, we felt putting something together should benefit a wide audience. 

“Python is now also the language of amateurs, and I mean that in the best possible way.”

— Guido van Rossum, creator of the Python programming language.

Despite its simplicity and versatility, Python is not exempt from security vulnerabilities. This guide will explore secure coding, common vulnerabilities, and the tools and libraries that can bolster your Python application's security.

Understanding Python Security

Understanding Python security is crucial for engineers to ensure the safety of their applications. Python security encompasses the measures taken to protect Python applications from threats and vulnerabilities. It involves secure coding practices, vulnerability detection, and the use of security tools and libraries. According to a 2020 report by Snyk, 84% of developers fix vulnerabilities in their code themselves, indicating the importance of understanding and implementing Python security best practices.

Python developers often encounter common security issues such as code injection, insecure dependencies, and weak encryption. Code injection occurs when an attacker injects malicious code into your application, potentially leading to data loss or server takeover. Insecure dependencies refer to the use of outdated or vulnerable third-party libraries, which can expose your application to risks. Weak encryption, on the other hand, can make your data easily decipherable to attackers.

Understanding these issues and how to mitigate them is a fundamental part of Python security. In the following sections, we will delve deeper into secure coding in Python, common Python security vulnerabilities, and the tools and libraries that can help fortify your Python applications.

Python Security Checklist

Ensuring the security of your Python applications is a continuous process that requires diligence and adherence to best practices. This Python security checklist serves as a guide to help you maintain the security of your applications.

  1. Secure Coding: Always adhere to secure coding principles. This includes validating inputs, handling exceptions properly, and avoiding the use of insecure functions.

  2. Vulnerability Awareness: Stay informed about the common security vulnerabilities in Python. Understand their causes, impacts, and how to mitigate them.

  3. Use Security Tools: Utilize Python security tools and libraries to enhance your application's security. These tools can help identify and fix security vulnerabilities in your code.

  4. Security Testing: Regularly perform security testing on your Python applications. This includes both static and dynamic testing to identify potential security issues.

  5. Regular Updates: Keep your Python environment and libraries up-to-date. Regular updates not only provide new features but also fix security vulnerabilities.

  6. Risk Management: Implement a risk management strategy. This includes identifying potential risks, assessing their impact, and developing mitigation strategies.

By following this checklist, Python developers can ensure that they are adhering to the best practices for Python security, thereby reducing the risk of security vulnerabilities in their applications. We will be expanding on some of these sections below.

Secure Coding in Python

Secure coding in Python is a fundamental aspect of Python security best practices. It involves writing code that is not only functional but also resistant to security vulnerabilities. The first step towards secure coding is understanding the potential security risks associated with Python. These include injection attacks, insecure direct object references, and security misconfigurations, among others.

This section will provide a comprehensive list of these practices, offering practical tips and guidelines.

Use of Built-in Security Features

“Python is often described as a 'batteries included' language, meaning it has a rich and versatile standard library that is immediately available to users.” - Guido Van Rossum

Python comes with built-in security features that can help protect your applications. These include the 'hashlib' for secure hashes and password encryption, and 'secrets' module for generating cryptographically strong random numbers for managing secrets.

Input Validation

One of the key principles of secure coding in Python is input validation. Always validate, sanitize, and encode user input to prevent injection attacks. Avoid using eval() function as it can execute arbitrary Python code, making your application vulnerable to code injection attacks like SQL injection, cross-site scripting (XSS), and remote code execution.

Secure Dependencies

Third-party modules can introduce vulnerabilities into your Python applications. Always use trusted and secure libraries. Regularly update these dependencies and remove unused ones.

Least Privilege Principle

Another important principle is the principle of least privilege. This means that a process should only have the permissions necessary to perform its function and no more. For instance, if a function only needs to read a file, it should not have write permissions to that file.

Follow the principle of least privilege. Every module or process should have the least authority necessary to perform its function. This can limit the damage from accidents or bugs.

Error Handling

Proper error handling can prevent the leakage of sensitive information. Avoid revealing too much information in error messages and log files.

Code Review and Testing

Regular code reviews and security testing can help identify and fix security vulnerabilities early in the development process. Use automated tools for static code analysis and dynamic testing.

Python Security Vulnerabilities

Python, like any other programming language, is susceptible to a variety of security vulnerabilities. These vulnerabilities can be exploited by attackers to compromise the security of Python applications, leading to data breaches, system crashes, and other adverse impacts.

Like mentioned previously, one common vulnerability in Python is the injection attack, where malicious code is inserted into a Python application, often through user input. This can lead to unauthorized access to data, data corruption, and even system takeover. To mitigate this vulnerability, it is crucial to validate and sanitize all user inputs.

Another common vulnerability is insecure direct object references (IDOR), where an attacker can manipulate references to internal objects. This can lead to unauthorized data access or manipulation. To prevent IDOR, it is important to implement access controls and to avoid exposing internal object references.

Other common Python vulnerabilities include cross-site scripting (XSS), where an attacker injects malicious scripts into web pages viewed by other users, and insecure deserialization, where untrusted data is deserialized by a Python application. To mitigate these vulnerabilities, it is important to sanitize all outputs and to validate and sanitize all serialized data.

By understanding these common Python vulnerabilities, their causes, and their potential impacts, engineers can take proactive steps to identify and mitigate these vulnerabilities, thereby enhancing the security of their Python applications.

Python Security Tools and Libraries

Python offers a variety of security tools and libraries that can significantly enhance the security of your applications. These tools and libraries are designed to help developers identify and mitigate potential security vulnerabilities in their Python code.

One such tool is Bandit, a Python security linter from PyCQA. Bandit scans your Python code for common security issues, such as the use of insecure functions and modules. It provides a detailed report of potential security vulnerabilities, helping you to identify and fix them before they can be exploited.

Another useful library is pycryptodome, a self-contained Python package for cryptography. It provides secure hash functions, public key and symmetric ciphers, and other cryptographic primitives. It's a great tool for implementing secure data encryption in your Python applications.

Lastly, OWASP Python Security Project is a valuable resource for Python developers. It provides a collection of scripts, modules, and tools for securing Python web applications. It also offers a wealth of information on Python security best practices, making it a must-have resource for any engineer working with Python.

Python Security Scanning and Testing

Python security scanning and testing are essential tools for identifying potential security vulnerabilities in Python code. They work by analyzing the codebase for known security issues, such as SQL injection, cross-site scripting (XSS), and insecure direct object references (IDOR).

These scanners are typically automated, making them a valuable asset in the development process. They can quickly scan large codebases, providing developers with a detailed report of potential vulnerabilities and their severity levels. This allows developers to prioritize and address the most critical issues first. These tests can be broadly categorized into static and dynamic testing.

There are several Python security scanners available, each with its own strengths and weaknesses. Static Application Security Testing (SAST) like Snyk, Semgrep, Sonarqube, and the likes are great at finding vulnerabilities in python. Some popular open-source options include Bandit, and Safety

On the other hand, Dynamic Application Security Testing (DAST) involves testing the application in its running state. This type of testing can help identify runtime errors and vulnerabilities that may not be visible in the source code.

Both SAST and DAST are essential for ensuring the security of Python applications. They complement each other and provide a comprehensive view of the application's security posture. It’s also important to not forget manual testing by developers and pentesters before releasing a new version. By regularly performing these tests, engineers can ensure that their Python applications are secure and free from vulnerabilities.

It's important to remember that these tools are not a silver bullet. They should be used as part of a comprehensive security strategy, alongside secure coding practices, regular updates, and risk management.

Python Security Updates and Risk Management

Regular Python security updates and effective risk management are crucial in maintaining the security of Python applications. Updates often include patches for known vulnerabilities, thus reducing the risk of exploitation. It's essential to keep your Python environment and libraries up-to-date to benefit from these security enhancements.

Risk management, on the other hand, involves identifying, assessing, and mitigating security risks. This process starts with a thorough understanding of your application's architecture and data flow. By identifying potential security risks, you can take proactive measures to mitigate them before they can be exploited.

One effective risk management strategy is to adopt a 'defense in depth' approach. This involves implementing multiple layers of security controls to protect against different types of threats. For instance, you might use input validation to prevent injection attacks, encryption to protect sensitive data, and intrusion detection systems to identify and respond to malicious activities.

Another important aspect of risk management is incident response. This involves having a plan in place to respond quickly and effectively to security incidents when they occur. A good incident response plan can help minimize the damage caused by a security breach and ensure that your application can recover quickly.


In conclusion, Python application security is a crucial aspect that demands a deep grasp of Python security best practices, vulnerabilities, and available security testing and risk management tools. By following the guidelines and tips outlined in this article, engineers can notably improve their Python application security and reduce potential security threats.

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