Picklescan Numpy Global Function Bypass in Deserialization

Executive Summary

This vulnerability exists in Picklescan versions before 0.0.25, where it fails to detect unsafe global functions in the Numpy library during static analysis.

Attackers can exploit this by crafting malicious pickle files that use numpy.testing._private.utils.runstring within the reduce method to import dangerous libraries such as os.

When such a malicious pickle file is loaded, it can execute arbitrary OS commands, effectively allowing code execution during deserialization.

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Detection Guidance

This vulnerability can be detected by scanning pickle files for the presence of unsafe global functions, specifically those involving the Numpy library's numpy.testing._private.utils.runstring function used within the reduce method.

Since Picklescan versions prior to 0.0.25 fail to detect this unsafe usage, it is recommended to use Picklescan version 0.0.25 or later which includes Numpy in its unsafe globals blacklist.

To detect malicious pickle files on your system, you can run Picklescan on suspect pickle files and verify if any unsafe globals related to Numpy are flagged.

• Use Picklescan 0.0.25 or later to scan pickle files: `picklescan scan <pickle_file>`
• Monitor network traffic for suspicious pickle file transfers, especially those containing references to numpy.testing._private.utils.runstring.

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Impact Analysis

This vulnerability can allow attackers to execute arbitrary code on your system when a malicious pickle file is deserialized.

Such code execution can lead to unauthorized actions, including running OS commands, which may compromise system integrity, confidentiality, and availability.

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Compliance Impact

This vulnerability allows attackers to execute arbitrary code during deserialization by bypassing static analysis in Picklescan. Such unauthorized code execution can lead to compromise of system integrity and potential unauthorized access to sensitive data.

Because of this, organizations relying on Picklescan for security in machine learning workflows or other applications may face increased risk of data breaches or supply chain attacks, which could result in non-compliance with data protection regulations such as GDPR or HIPAA that require safeguarding sensitive information and ensuring system security.

However, the provided information does not explicitly detail the direct impact on compliance with these standards.

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Mitigation Strategies

The immediate mitigation step is to upgrade Picklescan to version 0.0.25 or later, which includes Numpy in the unsafe globals blacklist and properly detects malicious pickle files exploiting this vulnerability.

Additionally, avoid loading pickle files from untrusted or unauthenticated sources, as deserializing malicious pickle files can lead to arbitrary code execution.

Implement network monitoring and restrict the transfer of pickle files to reduce the risk of receiving malicious payloads.

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