CVE-2026-100846

MONAI before 1.5.2 Remote Code Execution via Pickle Deserialization

Published Sep 27, 2026 CVSS 4.0 · 8.8 High EPSS < 1% KEV · Not listed
Description
MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py. The function reads a .pkl file and passes its contents to pickle.loads without validating the data source or content. If an application invokes algo_from_pickle on an attacker-supplied pickle file, an object defining __reduce__ is executed during deserialization, resulting in arbitrary code execution in the context of the application.

Analysis

AI-generated

Technical impact

The vulnerability exists in MONAI before 1.5.2 due to the usage of pickle.loads to deserialize arbitrary data in the algo_from_pickle function. An attacker can supply a crafted .pkl file containing an object with a __reduce__ method, causing pickle to instantiate the object and execute arbitrary code as the user running the MONAI application. This results in complete loss of confidentiality, integrity, and availability. The weakness corresponds to CWE‑502 (Deserialization of Untrusted Data).

Affected Systems

The affected product is MONAI (Project‑MONAI:MONAI) version 1.5.1 and earlier, including any deployments that import and run the algo_from_pickle routine. No specific sub‑modules other than the autoload3dseg utils Python file are affected. The advisory does not mention a broader product family or additional components.

Risk and Exploitability

The CVSS score of 8.8 indicates high severity, and the EPSS score is not available. The vulnerability is not listed in the CISA KEV catalog, suggesting no known active exploitation. However, any application that accepts and deserializes user‑supplied pickle files is a likely attack surface. Because the deserialization is performed without source validation, a local or remote attacker who can inject a .pkl file into the application’s file system or data flow can trigger execution of arbitrary Python code under the application’s environment.

Remediation

No solution or workaround provided in the CVE record.

OpenCVE Recommended Actions

  • Update MONAI to version 1.5.2 or later, removing the insecure algo_from_pickle usage.
  • If an update is not feasible, eliminate or disable calls to algo_from_pickle in the application and replace them with a secure file loading mechanism that does not use pickle.
  • As a temporary hardening measure, validate incoming .pkl files with a whitelist or reject all untrusted pickle data before deserialization.

Generated by OpenCVE AI on September 27, 2026 at 03:47 UTC.

Tracking

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Advisories

No advisories available
History
Type Values Removed Values Added
Metrics ssvc

{'options': {'Automatable': 'no', 'Exploitation': 'poc', 'Technical Impact': 'total'}, 'version': '2.0.3'}

Type Values Removed Values Added
Description MONAI before 1.5.2 contains a deserialization of untrusted data vulnerability in the algo_from_pickle function in monai/auto3dseg/utils.py. The function reads a .pkl file and passes its contents to pickle.loads without validating the data source or content. If an application invokes algo_from_pickle on an attacker-supplied pickle file, an object defining __reduce__ is executed during deserialization, resulting in arbitrary code execution in the context of the application.
Title MONAI before 1.5.2 Remote Code Execution via Pickle Deserialization
First Time appeared Project-monai
Project-monai monai
Weaknesses CWE-502
CPEs cpe:2.3:a:project-monai:monai:*:*:*:*:*:*:*:*
Vendors & Products Project-monai
Project-monai monai
References
Metrics cvssV3_1

{'score': 7.6, 'vector': 'CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:C/C:H/I:H/A:H'}

cvssV4_0

{'score': 8.8, 'vector': 'CVSS:4.0/AV:N/AC:H/AT:P/PR:H/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H'}

Weaknesses
  • CWE-502

    Deserialization of Untrusted Data

Sources

Status
PUBLISHED
Assigner
VulnCheck
Published
Updated
Oct 8, 2026, 3:22 PM
Reserved
Sep 27, 2026, 12:18 AM
Status
Analyzed
Published
Sep 27, 2026, 2:17 AM
Modified
Oct 8, 2026, 4:17 PM