CVE-2025-71373
Received Received - Intake

Operator.methodcaller Bypass in Picklescan Prior to 0.0.33

Vulnerability report for CVE-2025-71373, including description, CVSS score, EPSS score, affected products, exploitability, helpful resources, and attack-flow context.

Publication date: 2026-07-04

Last updated on: 2026-07-04

Assigner: VulnCheck

Description

picklescan before 0.0.33 fails to detect operator.methodcaller function calls in pickle files, allowing attackers to bypass security checks. Remote attackers can craft malicious pickle payloads using operator.methodcaller that execute arbitrary code when loaded, compromising systems relying on picklescan for validation.

CVSS Scores

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Meta Information

Published
2026-07-04
Last Modified
2026-07-04
Generated
2026-07-04
AI Q&A
2026-07-04
EPSS Evaluated
N/A
NVD
EUVD

Affected Vendors & Products

Currently, no data is known.

Helpful Resources

Exploitability

CWE
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KEV
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CWE ID Description
CWE-693 The product does not use or incorrectly uses a protection mechanism that provides sufficient defense against directed attacks against the product.

Attack-Flow Graph

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Executive Summary

CVE-2025-71373 is a vulnerability in the picklescan library versions before 0.0.33 where the library fails to detect the use of the Python built-in function operator.methodcaller in pickle files.

Attackers can exploit this by crafting malicious pickle payloads that embed operator.methodcaller within the reduce method. Because picklescan does not flag these payloads as dangerous, victims may unknowingly load them using pickle.load(), which leads to remote code execution.

This vulnerability is significant for users relying on picklescan to validate pickle files, especially in contexts like PyTorch models, APIs, or saved Python objects.

Impact Analysis

This vulnerability allows remote attackers to bypass security checks performed by picklescan and execute arbitrary code on affected systems.

If you rely on picklescan to validate pickle files before loading them, attackers can craft malicious pickle files that appear safe but execute harmful code when loaded.

This can lead to system compromise, unauthorized actions, or disruption of services, especially in environments using machine learning models, APIs, or other Python objects that utilize pickle serialization.

Detection Guidance

This vulnerability involves picklescan versions prior to 0.0.33 failing to detect the use of the operator.methodcaller function in pickle files, which can be exploited to execute arbitrary code.

To detect this vulnerability on your system, you should check the version of picklescan installed and inspect pickle files for the presence of operator.methodcaller usage within the reduce method.

Suggested commands include:

  • Check picklescan version: `pip show picklescan` or `pip list | grep picklescan`
  • Manually inspect pickle files for suspicious usage of operator.methodcaller by loading them in a controlled environment or using custom scripts to parse pickle contents.
  • Use updated picklescan (version 0.0.33 or later) to scan pickle files, as earlier versions fail to detect this payload.
Mitigation Strategies

The primary mitigation step is to upgrade picklescan to version 0.0.33 or later, where the detection of operator.methodcaller function calls in pickle files has been fixed.

Additionally, avoid loading pickle files from untrusted or unauthenticated sources, as malicious payloads can execute arbitrary code when loaded.

Consider implementing additional validation or sandboxing mechanisms when handling pickle files to reduce the risk of remote code execution.

Compliance Impact

The vulnerability allows attackers to bypass security checks and execute arbitrary code by exploiting picklescan's failure to detect operator.methodcaller function calls in pickle files. This can lead to compromise of systems relying on picklescan for validation.

Such a compromise could potentially result in unauthorized access to sensitive data or disruption of services, which may impact compliance with data protection regulations like GDPR or HIPAA that require safeguarding data integrity and confidentiality.

However, the provided information does not explicitly mention direct effects on compliance with these standards or specific regulatory requirements.

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