Evidence used
- No CISA KEV confirmation is currently recorded.
- EPSS is 0.38% for the current model date.
BlackTreeCVE IntelligenceFull vulnerability report · 2026
CVE-2026-7584High confidence
Arbitrary Code Execution via Unsafe Deserialization in LabOne Q
Zurich Instruments · LabOne Q
8.4HighCVSS 4.0
Within 7 days
Patch availableHigh technical severity; prioritise exposed affected systems while verifying vendor guidance.
R
Open reassessment dashboard →Operational reassessment
Published severity in operational context
Published severityHigh→Operational priority:High, unchanged from published severity.unchanged
Compensating controls
- Validate the affected product branch and deploy the verified fixed release.
- Restrict local access and enforce least privilege on affected hosts.
- Monitor vendor guidance and exploitation sources for a material change.
Verification
- Confirm that the asset runs Zurich Instruments LabOne Q and falls inside the recorded affected range.
- Verify the installed build against the product-specific fixed version after deployment.
- Validate exposure, authentication requirements and compensating controls in the actual environment.
- Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
Patch available01
What, why and how
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.
What
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.
Why
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
How
An attacker operating through local access may attempt exploitation when the stated preconditions are met. If successful, the issue may execute code or commands in the affected security context.
What
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.
Why
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
How
An attacker operating through local access may attempt exploitation when the stated preconditions are met. If successful, the issue may execute code or commands in the affected security context.
02
Exploit reality and attack path
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
Observed exploitation No confirmed evidence
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
Public PoC / exploit material None recorded
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
- Likely attack path
- local access → Deserialization of Untrusted Data → execute code or commands in the affected security context
- Attack surface
- Local
- Privileges required
- None: unauthenticated exploitation is possible
- User interaction
- Active interaction required
- Attack complexity
- Low: no specialised conditions are recorded
- Security boundary
- Not a CVSS 4.0 base metric
- Weakness
?
CWE means Common Weakness Enumeration: a standard category for the underlying weakness. - CWE-502 ↗
CWE-502: Deserialization of Untrusted Data. The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
- CVSS vector
?
CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity. CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:NCommon Vulnerability Scoring System 4.0: the compact vector below is decoded into plain language.
AVLocalAttack vector: The attacker needs local access to the vulnerable system.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.ATNoneAttack requirements: No additional deployment or execution condition is required.PRNonePrivileges required: The attacker does not need an account or existing privileges.UIActiveUser interaction: A user must take a deliberate action for exploitation to succeed.VCHighVulnerable-system confidentiality: A successful attack can cause a major loss.VIHighVulnerable-system integrity: A successful attack can cause a major loss.VAHighVulnerable-system availability: A successful attack can cause a major loss.SCNoneSubsequent-system confidentiality: No direct loss is represented by this metric.SINoneSubsequent-system integrity: No direct loss is represented by this metric.SANoneSubsequent-system availability: No direct loss is represented by this metric.- Post-exploitation / living off the land
- After compromise, an attacker may use built-in shells, scripting engines, scheduled tasks and native network utilities for discovery, persistence or movement. This is a plausible LoTL path, not evidence that it has occurred for every attack.
03
Patch and workaround
Operational remediation based on structured source evidence.
- Status
?
Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change. - Patch available
- Action
- Review the linked authoritative reference and apply the recorded fixed release appropriate to the affected product branch.
- Workaround
- No verified workaround is recorded. Limit untrusted access and use least privilege until authoritative guidance is available.
04
Evidence and provenance
Published 1 May 2026 · Last source change 1 May 2026, 13:26 UTC · CWE-502 · Deserialization of Untrusted Data
CVE recordCVE.org · 5.2
CVSS sourceCNA
EPSS source FIRST · tracked since 2026-08-14
?
The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.European sourceENISA EUVD · EUVD-2026-26483
Product sourceCNA
Remediation sourceCVE/CNA references
CWE sourceCNA
NVD statusNVD enriched
Core structured fields are present and their contributing authorities are shown above.
Material change intelligence
View recent updates ↗What changed after publication
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.
Technical terms and abbreviations used in this report
- CVE
- Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
- CVSS
- Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
- EPSS
- Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
- CWE
- Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
- CNA
- CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
- CISA ADP
- Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
- NVD
- National Vulnerability Database: NIST's enrichment service for CVE records.
- CERT / CSIRT
- A computer security incident response team that publishes warnings or coordinates incident response.
- PoC
- Proof of concept: public material that demonstrates or helps reproduce exploitation.
- CSAF
- Common Security Advisory Framework: a machine-readable format for security advisories.
- LoTL
- Living off the land: abuse of legitimate tools or system functions during an attack.