Write better code
Check your Python code security before your next PR commit and get alerts of critical bugs using our free Python code checker — powered by Snyk Code.
Sign up for free to unlock the the full power of Snyk, no credit card required.
To take your application security to the next level by using Snyk Code for free right from your IDE.
File I/O corruptions
API contract violations
Null dereferences
Process/threading deadlock problems
Incorrect type checking
Expression logic mistakes
Regular expression denial of service
Invalid time/date formatting
Resource leaks
Missing input data sanitization
Insecure password handling
Protocol insecurities
Indefensive permissions
Man-in-the-Middle attacks
Weak cryptography algorithms
Information disclosure
Code injection
SQL injection
Sign up now to get access to all the features including vulnerability alerts, real time scan results, and actionable fix advice within your IDE.
Snyk Code is an expert-curated, AI-powered Python code checker that analyzes your code for security issues, providing actionable advice directly from your IDE to help you fix vulnerabilities quickly.
Scan and fix source code in minutes.
Fix vulns with dev friendly remediation.
Find vulns early to save time & money.
Integrates into existing workflow.
Comprehensive semantic analysis.
Modern ML directed by security experts.
Automatically scan every PR and repo.
Integrate scans into the build process.
An AI-powered Python code checker allows organizations to detect and remediate more complex code issues earlier in the secure software development lifecycle (SSDLC). AI algorithms that have been trained by hundreds of thousands of open source projects to capture symbolic AI rules about possible issues and remediation. By leveraging this learned knowledge from the global open source development community, an AI engine can often detect quality and security issues that may not be caught during peer code reviews or pair programming. That means the efficiency of an AI-powered Python code checker enables developers to fix issues very early — before they reach production and potentially impact end-users.
A key part of DevSecOps is shifting left — or detecting and remediating vulnerabilities earlier in the development process. Implementing a Python code checker into your existing continuous integration and continuous delivery (CI/CD) pipeline is one of the most widely accepted best practices. Embedding static analysis into the IDE informs developers of Python vulnerabilities at the earliest possible moment — eliminating Python code security risks at the source.
A Python syntax error is an issue that occurs when Python code is interpreted during execution. Syntax errors are one of three basic types of error, and are almost always fatal because the Python interpreter cannot understand a line of code. Logic errors occur when the code is valid, but the application doesn’t do what the developer intended. Exceptions occur when the Python parser understands a line of code, but the interpreter is unable to execute it during runtime.
There are a variety of syntax and logical errors, so it’s important to know how to remediate the most common issues that a debugger or code checker may flag. While logical errors aren’t recognized by the Python interpreter, they still prevent the application from performing as the developer originally intended. Here are some tips to avoid some common logical flaws when writing Python code:
Remember to invoke a function to start the execution of the program.
Check for infinite loops where the program gets stuck in a recurring code block.
Use print statements to understand the flow of execution and ensure it’s correct.
Avoid complex expressions that make code harder to read and debug.
Integrating a Python code checker into the existing developer workflow is a great way to fix code issues earlier, while also helping developers learn about best practices. This can make a significant impact on the quality and security of Python code that developers write going forward. More maintainable code can also improve the customer experience because there are fewer bugs and technical debt to deal with in the future.
When it comes to static application security testing (SAST) with a Python code checker, it’s important to choose a developer-first tool that integrates into developer workflows and produces minimal false positives in scan results. A SAST tool also needs to take a comprehensive approach for scanning source code, and be able to combine with linters to check code syntax and style.
The most common types of SAST security analysis are:
CONFIGURATION:
Ensures that application configuration files are following security best practices and policies.
SEMANTIC:
Examines code contextually to estimate what the developer intended, and check whether the code syntax differs.
DATA FLOW:
Tracks the flow of data from insecure sources to ensure it’s cleansed before consumption by the Python application.
STRUCTURAL:
Determines whether there are inconsistencies with implementing language-specific best practices and cryptographic techniques.
The Python code checker you use should also leverage a comprehensive vulnerability database to identify security issues at the code level, as well as known vulnerabilities introduced via open source dependencies.Vulnerability databases help developers stay on top of the latest security exploits as they’re discovered, without spending endless hours researching the current cyber threat landscape. This type of data-driven security works in tandem with threat intelligence to improve the overall security posture of your organization.
Finally, detecting Python code security issues is only half the battle. An effective code checker solution will identify flaws, while also giving developers the insights they need to remediate them. This should include the precise source of the issue, and any known publicly available fixes for both security flaws and code anti-patterns.
Python code security can be described using the CIA triad — confidentiality, integrity, and availability. The CIA triad is often used as a model for secure systems, and to identify possible vulnerabilities and fixes. Today, applications consist of 80 to 90% open source dependencies. But the remaining 10 to 20% is critical: this code reflects your personal IP, and there is no open source community helping you keep it secure. The best practice is to accept the work of the open source community by scanning and updating software dependencies in your project using scanners like Snyk Open Source — while doing your part by scanning and fixing your code using Snyk Code.
Confidentiality
Secure software systems do not disclose information to parties that are not allowed to receive it. That includes malicious external actors as well as unauthorized internal stakeholders.
Integrity
Secure software systems make sure that data and processes are not tempered with, destroyed, or altered. Transactions succeed when all sub-transactions succeed, and the stored data does not contradict each other.
Availability
A secure system also needs to be able to be used in due time. Blocking a system by overloading parts of it renders the system useless and insecure.
Python code quality is a subjective term, and means something different to every development team. In general, however, the quality of code relates to how closely it follows commonly accepted coding standards and best practices. Here are five frequently used measures of code quality to consider when developers ask, how do I check my Python code?
Reusability It’s best to write code that’s highly reusable. For example, in object-oriented programming, it’s important to make classes and methods clean and modular, so that code is easier to debug and scale across projects. Restricting access to certain reusable blocks of code through encapsulation can also improve security.
Maintainability Along with being reusable, it’s important that Python source code is maintainable. As a codebase grows, complexity and technical debt often increase, leading to bugs that are difficult to pinpoint and slow development in the long run. Automated code analysis and peer reviews can ensure that developers are only pushing highly maintainable code into production.
Testability High-quality Python code should support testing efforts. Along with writing modular code that makes automated testing easier, developers need to prioritize clear and up-to-date documentation. This allows test engineers to more easily understand the purpose of a particular code snippet.
Consistency Python code should be portable enough that it can run on any development, staging, or production environment without compatibility issues. Docker and other containerization platforms can help ensure Python code and dependencies are consistent across different deployment environments.
Reliability Software should be designed for reliability from the start. Meaning developers need to proactively prevent technical debt from accruing when they push Python code. Otherwise, software can become less reliable over time and have a decrease in availability, fault tolerance, data integrity, and ability to recover from outages. These lack of reliability can also have a negative impact on the security posture of an application.
Perform a semantic check and secure your Python code in your IDE.
Secure your code as you develop. Snyk’s free IDE plugins scan your Python code for vulnerabilities in real-time and provide fix advice.