Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Snyk Security Researchers have been using dynamic analysis techniques to unravel the behaviors of obfuscated malicious packages. A recent interesting finding in the Python Package Index (PyPi) attempted to imitate a known open source developer through identity spoofing. Upon further analysis, the team uncovered that the package, raw-tool, was attempting to hide malicious behavior using base64 encoding, reaching out to malicious servers, and executing obfuscated code.

There are many ways to find bugs in Python code: the built-in debugger (pdb), a healthy amount of unit tests, a debugger in an IDE like Pycharm or Visual Studio, try/catch statements, if/else statements, assert statements, or the tried and true practice of covering every inch of your codebase in print() statements like it’s going out of style. Assert statements can help us catch bugs quickly and are far less intrusive than copious amounts of print statements.

Typically, when a web app needs something from an external server, the client sends a request to that server, the server responds, and the connection is subsequently closed. Consider a web app that shows stock prices. The client must repeatedly request updated prices from the server to provide the latest prices.

The software supply chain remains a weak link for an attacker to exploit and gain access to an organization. According to a report in 2021, supply chain attacks increased by 650%, and some of the attacks have received a lot of limelight, such as SUNBURST in 2020 and Dependency Confusion in 2021.

While most developers — myself included — primarily write in higher-level languages like Python or JavaScript, sometimes you need to add in native elements to improve performance or other project aspects. Since these native extension invocations are typically written in C or C++, suddenly a project primarily using JavaScript or Python must also account for potential C/C++ transient dependencies.

Snyk recently open sourced our faker-security Python package to help anyone working with security data. In this blog post, we’ll briefly go over what this Python package is and how to use it. But first, we’ll get some context for how the factory_boy Python package can be used in combination with faker-security to improve your test-writing experience during development. Note: Some knowledge of Python is helpful for getting the most out of this post.

During application development, we often need to persist complex data (like objects) for use in different runtimes. However, maintaining persistence within complex data structures and objects is far from straightforward. In Python, you can use the built-in pickle library to handle this process. Pickle can serialize a Python object into a flat byte stream (pickling) as well as transform a byte stream back into a Python object (unpickling).

I conducted research based upon existing Python vulnerabilities and identified a common software pattern between them. By utilizing the power of our in-house static analysis engine, which also drives Snyk Code, our static application security testing (SAST) product, I was able to create custom rules and search across a large dataset of open source code, to identify other projects using the same pattern. This led to the discovery of a stored command injection vulnerability in Celery.

The JFrog Security research team continuously monitors popular open source software (OSS) repositories with our automated tooling to detect and avert potential software supply chain security threats. After validating the findings, the team reports any security vulnerabilities or malicious packages discovered to repository maintainers and the wider community.