Malicious Code Vulnerability in tableasets Package
Summary
MAL-2026-602: Malicious code in tableasets (PyPI). Affects tableasets versions 1.0.1. No fix is published. How to fix ↓
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Background
The tableasets package, available on PyPI, is designed for handling and manipulating tabular datasets in Python. It provides various functionalities for data analysis and processing. However, this package has been compromised, containing hidden malicious code that executes upon import, posing significant security risks to users.
Vulnerability Detail
The vulnerability originates from a malicious campaign identified as '2025-11-spellcheckers'. The compromised version of the tableasets package includes obfuscated code that downloads and executes a second-stage malicious script. This script establishes a connection to a remote host, allowing the attacker to execute commands on the victim's machine. The implications of this vulnerability are severe, as it can lead to unauthorized access, data theft, and further exploitation of the affected systems.
How To Fix
This package is malicious. Remove it and rotate any credentials it could have accessed.
pip uninstall tableasetsIf it arrived as a transitive dependency, remove the package that pulled it in, then reinstall.
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Workaround
As a temporary workaround, users can avoid using the tableasets package until a safe version is confirmed. Additionally, consider using alternative libraries for handling tabular data, such as pandas or numpy, which are widely used and trusted.
Conclusion
The tableasets package has been compromised with malicious code that poses significant security risks. Users are advised to uninstall the affected version and consider alternative libraries for their data handling needs. Regular monitoring of dependencies is crucial to maintain security.
See every vulnerability recorded for tableasets →
FAQ
The malicious code is designed to run during the import of the package, downloading additional scripts and allowing remote command execution.
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Uninstall the tableasets package immediately and check for any signs of unauthorized access on your system.
Yes, consider using libraries like pandas or numpy for data manipulation and analysis, which are well-established and secure.
Utilize tools like Vulert to monitor your dependencies for vulnerabilities and receive alerts in real-time.
