The LabOne Q serialization framework uses a class-loading mechanism (import_cls) to dynamically import and instantiate Python classes during deserialization. Prior to the fix, this mechanism accepted arbitrary fully-qualified class names from the serialized data without any validation of the target class or restriction on which modules could be imported. An attacker can craft a serialized experiment file that causes the deserialization engine to import and instantiate arbitrary Python classes with attacker-controlled constructor arguments, resulting in arbitrary code execution in the context of the user running the Python process. Exploitation requires the victim to load a malicious file using LabOne Q's deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.

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Advisories

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Fixes

Solution

Update LabOne Q to version 26.1.2 (security backport on the 26.1.x line) or to 26.4.0 or later. The package can be updated via `pip install --upgrade laboneq`.


Workaround

Do not load untrusted experiment files: only deserialize experiment files (JSON, YAML) that originate from a trusted source. Treat serialized experiment files with the same caution as executable scripts. Validate file provenance: when receiving experiment files from external parties (e.g. for support or collaboration), verify their origin before loading them. Audit serialized files: before loading, inspect serialized experiment files and verify that only trusted classes are listed as deserializers.

History

Fri, 01 May 2026 14:30:00 +0000

Type Values Removed Values Added
Metrics ssvc

{'options': {'Automatable': 'no', 'Exploitation': 'none', 'Technical Impact': 'total'}, 'version': '2.0.3'}


Fri, 01 May 2026 07:45:00 +0000

Type Values Removed Values Added
Description The LabOne Q serialization framework uses a class-loading mechanism (import_cls) to dynamically import and instantiate Python classes during deserialization. Prior to the fix, this mechanism accepted arbitrary fully-qualified class names from the serialized data without any validation of the target class or restriction on which modules could be imported. An attacker can craft a serialized experiment file that causes the deserialization engine to import and instantiate arbitrary Python classes with attacker-controlled constructor arguments, resulting in arbitrary code execution in the context of the user running the Python process. Exploitation requires the victim to load a malicious file using LabOne Q's deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.
Title Arbitrary Code Execution via Unsafe Deserialization in LabOne Q
Weaknesses CWE-502
References
Metrics cvssV3_1

{'score': 7.8, 'vector': 'CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H'}

cvssV4_0

{'score': 8.4, 'vector': 'CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N'}


Projects

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cve-icon MITRE

Status: PUBLISHED

Assigner: NCSC.ch

Published:

Updated: 2026-05-01T13:26:59.075Z

Reserved: 2026-05-01T07:14:23.592Z

Link: CVE-2026-7584

cve-icon Vulnrichment

Updated: 2026-05-01T13:26:54.611Z

cve-icon NVD

Status : Awaiting Analysis

Published: 2026-05-01T08:16:01.913

Modified: 2026-05-01T15:28:29.083

Link: CVE-2026-7584

cve-icon Redhat

No data.

cve-icon OpenCVE Enrichment

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Weaknesses