picklescan up to 0.0.32 numpy.f2py.crackfortran deserialization

Summaryinfo

A vulnerability was found in picklescan up to 0.0.32. It has been rated as critical. This impacts the function numpy.f2py.crackfortran. The manipulation leads to deserialization. This vulnerability is uniquely identified as CVE-2025-71362. The attack is possible to be carried out remotely. No exploit exists. Upgrading the affected component is advised.

Detailsinfo

A vulnerability was found in picklescan up to 0.0.32 and classified as critical. This issue affects the function numpy.f2py.crackfortran. The manipulation with an unknown input leads to a deserialization vulnerability. Using CWE to declare the problem leads to CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Impacted is confidentiality, integrity, and availability. The summary by CVE is:

picklescan before 0.0.33 fails to detect unsafe deserialization when numpy.f2py.crackfortran functions call eval on arbitrary strings. Attackers can embed malicious code in pickle files that executes when loaded from untrusted sources.

It is possible to read the advisory at github.com. The identification of this vulnerability is CVE-2025-71362 since 06/20/2026. The exploitation is known to be easy. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. It demands that the victim is doing some kind of user interaction. Technical details of the vulnerability are known, but there is no available exploit.

Upgrading to version 0.0.33 eliminates this vulnerability.

The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2025-210418). Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Name

• picklescan

Version

• 0.0.1
• 0.0.2
• 0.0.3
• 0.0.4
• 0.0.5
• 0.0.6
• 0.0.7
• 0.0.8
• 0.0.9
• 0.0.10
• 0.0.11
• 0.0.12
• 0.0.13
• 0.0.14
• 0.0.15
• 0.0.16
• 0.0.17
• 0.0.18
• 0.0.19
• 0.0.20
• 0.0.21
• 0.0.22
• 0.0.23
• 0.0.24
• 0.0.25
• 0.0.26
• 0.0.27
• 0.0.28
• 0.0.29
• 0.0.30
• 0.0.31
• 0.0.32

CPE 2.3info

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CPE 2.2info

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Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

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