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CVE-2026-7584 (GCVE-0-2026-7584)

Vulnerability from cvelistv5 – Published: 2026-05-01 07:21 – Updated: 2026-05-01 13:26
VLAI
Title
Arbitrary Code Execution via Unsafe Deserialization in LabOne Q
Summary
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.
Severity
7.8 (High)
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
8.4 (High)
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
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-01 13:26 UTC
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
References
Impacted products
Vendor Product Version CPE status
Zurich Instruments LabOne Q Affected: 2.41.0 , < 26.1.2 (python)
Affected: 26.4.0b1 , ≤ 26.4.0b5 (python)
guessed Create a notification for this product.
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            }
          ],
          "source": {
            "discovery": "INTERNAL"
          },
          "title": "Arbitrary Code Execution via Unsafe Deserialization in LabOne Q",
          "workarounds": [
            {
              "lang": "en",
              "supportingMedia": [
                {
                  "base64": false,
                  "type": "text/html",
                  "value": "\u003cp\u003eDo 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.\u003c/p\u003e\u003cp\u003eValidate file provenance: when receiving experiment files from external parties (e.g. for support or collaboration), verify their origin before loading them.\u003c/p\u003e\u003cp\u003eAudit serialized files: before loading, inspect serialized experiment files and verify that only trusted classes are listed as deserializers.\u003c/p\u003e"
                }
              ],
              "value": "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.\n\n\n\nValidate file provenance: when receiving experiment files from external parties (e.g. for support or collaboration), verify their origin before loading them.\n\n\n\nAudit serialized files: before loading, inspect serialized experiment files and verify that only trusted classes are listed as deserializers."
            }
          ],
          "x_generator": {
            "engine": "Vulnogram 1.0.2"
          }
        }
      },
      "cveMetadata": {
        "assignerOrgId": "455daabc-a392-441d-aa46-37d35189897c",
        "assignerShortName": "NCSC.ch",
        "cveId": "CVE-2026-7584",
        "datePublished": "2026-05-01T07:21:18.781Z",
        "dateReserved": "2026-05-01T07:14:23.592Z",
        "dateUpdated": "2026-05-01T13:26:59.075Z",
        "state": "PUBLISHED"
      },
      "dataType": "CVE_RECORD",
      "dataVersion": "5.2"
    }
  }
}



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Taxonomy of the tags.





Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.


Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.