CVE-2026-7584
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedThe 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.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysis · high confidenceLabOne Q's serialization framework uses an unsafe class-loading mechanism (import_cls) that accepts arbitrary fully-qualified class names from serialized data without validation. During deserialization, the framework imports and instantiates these attacker-specified classes with controlled constructor arguments, enabling arbitrary code execution in the user's context.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.
NVD · CPE data>= 2.41.0, < 26.1.2= 26.4.0CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Local
- Complexity
- Low
- Privileges
- None
- User interaction
- Required
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.
dbcve checksWork through these to decide whether this CVE applies to you.
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Check LabOne Q versionRun 'laboneq --version' or check the LabOne Q installation directory for version information. Common paths include /opt Zurich Instruments/LabOneQ/ or check the Python package with 'pip show laboneq'.Affected if The installed version is >= 2.41.0 and < 26.1.2, or exactly 26.4.0
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Identify import_cls usage in serialization configSearch for 'import_cls' in LabOne Q configuration files, experiment definitions, or Python scripts that load LabOne Q experiment files. Check .seqc files and any Python code that uses laboneq.serialization.load() or similar deserialization functions.Affected if The import_cls mechanism is present and accepts class names from serialized data without validation in your configuration or loaded experiment files
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Check deserialization of external experiment filesReview any automated or manual processes that load LabOne Q experiment files (.seqc, .json experiment definitions) from external or untrusted sources. Look for calls to deserialization functions such as laboneq.core.serialization.from_byte_array() or experiment loading functions.Affected if Your LabOne Q setup deserializes experiment files that may contain attacker-controlled class names in the import_cls field
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Audit Python environment for unsafe deserializationInspect Python code using LabOne Q for patterns like unpickling, yaml.load with unsafe modes, or custom deserialization that handles LabOne Q experiment data. Search for usage of the LabOne Q serialization module.Affected if Your code uses LabOne Q's serialization framework to load data from untrusted sources without class validation
You are affected if LabOne Q version is >= 2.41.0 and < 26.1.2 (or exactly 26.4.0) AND your environment loads experiment files or serialized data using LabOne Q's import_cls mechanism.
Generated from the published advisory. Verify against your own configuration.
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
dbcve · scoped26.1.2
Restrict the import_cls mechanism to a whitelist of expected classes and validate class names before loading. Users should avoid loading experiment files from untrusted sources until the fix is applied.
LabOne Q >= 26.1.2 (excluding 26.4.0), recommended latest stable release
- 1. Identify the current LabOne Q version by checking the installed package or documentation
- 2. If running version < 26.1.2, upgrade directly to version 26.1.2 or later (but not 26.4.0)
- 3. If running version 26.4.0, upgrade to version 26.4.1 or later
- 4. Verify the upgrade by checking the new version number after installation
- 5. Test that legitimate experiment files still load correctly after the upgrade
Generated from the published advisory — verify against the referenced sources before acting.
- Consultation2.0 h
- Implementation8.0 h
- Testing4.0 h
- Review / QA2.0 h
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Scan for this in your stack
Free · runs locallyCheck whether your project pulls in CVE-2026-7584 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2026-7584 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Version or environment caveats, and links to real fixes
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