Executive Summary

CVE-2025-71354 is a vulnerability in picklescan versions before 0.0.29 where the tool fails to detect malicious pickle files that exploit the idlelib.debugobj.ObjectTreeItem.SetText function within reduce methods.

Attackers can craft specially designed pickle files containing embedded code that bypasses picklescan's detection mechanisms. When these files are loaded using pickle.load(), the embedded code executes arbitrary commands, leading to remote code execution.

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Impact Analysis

This vulnerability can lead to remote code execution on systems using vulnerable versions of picklescan to scan pickle files.

Attackers can distribute malicious pickle files that execute arbitrary commands when loaded, potentially compromising the security of applications that rely on picklescan for detecting malicious pickle files.

This is particularly impactful in environments where pickle files are used to store or transfer machine learning models, APIs, or saved Python objects, enabling supply chain attacks.

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Detection Guidance

This vulnerability involves malicious pickle files that exploit the idlelib.debugobj.ObjectTreeItem.SetText function in reduce methods, bypassing picklescan detection. Detection requires analyzing pickle files for suspicious reduce method payloads that execute arbitrary code.

Since picklescan versions before 0.0.29 fail to detect these malicious pickle files, the primary detection method is to use an updated picklescan tool (version 0.0.29 or later) to scan pickle files.

Suggested command to detect malicious pickle files using the updated picklescan tool:

• picklescan scan <path_to_pickle_file_or_directory>

If you suspect malicious pickle files on your system or network, scan all pickle files or directories containing pickle files with the updated picklescan tool to identify files that contain embedded code exploiting this vulnerability.

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Mitigation Strategies

The immediate mitigation step is to upgrade picklescan to version 0.0.29 or later, where this vulnerability has been patched.

Avoid loading pickle files from untrusted or unknown sources using pickle.load(), as malicious pickle files can execute arbitrary code.

Implement strict validation and scanning of pickle files before processing them in your environment to prevent exploitation.

Monitor and audit usage of pickle files in your systems, especially in machine learning models, APIs, or saved Python objects, to detect and prevent supply chain attacks.

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Compliance Impact

CVE-2025-71354 allows attackers to execute arbitrary code by bypassing detection of malicious pickle files, which can lead to unauthorized access or manipulation of data.

Such unauthorized code execution and potential data compromise could impact compliance with data protection regulations like GDPR and HIPAA, which require safeguarding sensitive data against unauthorized access and ensuring data integrity.

Organizations using vulnerable versions of picklescan may face increased risk of data breaches or supply chain attacks, potentially resulting in non-compliance with these standards.

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