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
CWE
  • CWE-502 - Deserialization of Untrusted Data
Assigner
References
Impacted products
Vendor Product Version
Zurich Instruments LabOne Q Affected: 2.41.0 , < 26.1.2 (python)
Affected: 26.4.0b1 , ≤ 26.4.0b5 (python)
Create a notification for this product.
Show details on NVD website
To clipboard 

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2026-7584",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-05-01T13:26:46.982666Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-05-01T13:26:59.075Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "collectionURL": "https://pypi.org/project/laboneq/",
          "defaultStatus": "unaffected",
          "packageName": "laboneq",
          "product": "LabOne Q",
          "vendor": "Zurich Instruments",
          "versions": [
            {
              "lessThan": "26.1.2",
              "status": "affected",
              "version": "2.41.0",
              "versionType": "python"
            },
            {
              "lessThanOrEqual": "26.4.0b5",
              "status": "affected",
              "version": "26.4.0b1",
              "versionType": "python"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "supportingMedia": [
            {
              "base64": false,
              "type": "text/html",
              "value": "\u003cp\u003eThe 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\u0027s deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.\u003c/p\u003e"
            }
          ],
          "value": "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\u0027s deserialization functions, for example a compromised experiment file shared for collaboration or support purposes."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 7.8,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "format": "CVSS",
          "scenarios": [
            {
              "lang": "en",
              "value": "GENERAL"
            }
          ]
        },
        {
          "cvssV4_0": {
            "Automatable": "NOT_DEFINED",
            "Recovery": "NOT_DEFINED",
            "Safety": "NOT_DEFINED",
            "attackComplexity": "LOW",
            "attackRequirements": "NONE",
            "attackVector": "LOCAL",
            "baseScore": 8.4,
            "baseSeverity": "HIGH",
            "exploitMaturity": "NOT_DEFINED",
            "privilegesRequired": "NONE",
            "providerUrgency": "NOT_DEFINED",
            "subAvailabilityImpact": "NONE",
            "subConfidentialityImpact": "NONE",
            "subIntegrityImpact": "NONE",
            "userInteraction": "ACTIVE",
            "valueDensity": "NOT_DEFINED",
            "vectorString": "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",
            "version": "4.0",
            "vulnAvailabilityImpact": "HIGH",
            "vulnConfidentialityImpact": "HIGH",
            "vulnIntegrityImpact": "HIGH",
            "vulnerabilityResponseEffort": "NOT_DEFINED"
          },
          "format": "CVSS",
          "scenarios": [
            {
              "lang": "en",
              "value": "GENERAL"
            }
          ]
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-502",
              "description": "CWE-502 Deserialization of Untrusted Data",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-05-01T07:21:18.781Z",
        "orgId": "455daabc-a392-441d-aa46-37d35189897c",
        "shortName": "NCSC.ch"
      },
      "references": [
        {
          "name": "ZI-SA-2026-002",
          "tags": [
            "vendor-advisory"
          ],
          "url": "https://www.zhinst.com/support/security/2026/zi-sa-2026-002/"
        }
      ],
      "solutions": [
        {
          "lang": "en",
          "supportingMedia": [
            {
              "base64": false,
              "type": "text/html",
              "value": "\u003cp\u003eUpdate 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`.\u003c/p\u003e"
            }
          ],
          "value": "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`."
        }
      ],
      "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",
  "vulnerability-lookup:meta": {
    "epss": {
      "cve": "CVE-2026-7584",
      "date": "2026-05-08",
      "epss": "0.0004",
      "percentile": "0.11983"
    },
    "nvd": "{\"cve\":{\"id\":\"CVE-2026-7584\",\"sourceIdentifier\":\"vulnerability@ncsc.ch\",\"published\":\"2026-05-01T08:16:01.913\",\"lastModified\":\"2026-05-04T18:23:58.433\",\"vulnStatus\":\"Analyzed\",\"cveTags\":[],\"descriptions\":[{\"lang\":\"en\",\"value\":\"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\u0027s deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.\"}],\"metrics\":{\"cvssMetricV40\":[{\"source\":\"vulnerability@ncsc.ch\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"4.0\",\"vectorString\":\"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/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X\",\"baseScore\":8.4,\"baseSeverity\":\"HIGH\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"attackRequirements\":\"NONE\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"ACTIVE\",\"vulnConfidentialityImpact\":\"HIGH\",\"vulnIntegrityImpact\":\"HIGH\",\"vulnAvailabilityImpact\":\"HIGH\",\"subConfidentialityImpact\":\"NONE\",\"subIntegrityImpact\":\"NONE\",\"subAvailabilityImpact\":\"NONE\",\"exploitMaturity\":\"NOT_DEFINED\",\"confidentialityRequirement\":\"NOT_DEFINED\",\"integrityRequirement\":\"NOT_DEFINED\",\"availabilityRequirement\":\"NOT_DEFINED\",\"modifiedAttackVector\":\"NOT_DEFINED\",\"modifiedAttackComplexity\":\"NOT_DEFINED\",\"modifiedAttackRequirements\":\"NOT_DEFINED\",\"modifiedPrivilegesRequired\":\"NOT_DEFINED\",\"modifiedUserInteraction\":\"NOT_DEFINED\",\"modifiedVulnConfidentialityImpact\":\"NOT_DEFINED\",\"modifiedVulnIntegrityImpact\":\"NOT_DEFINED\",\"modifiedVulnAvailabilityImpact\":\"NOT_DEFINED\",\"modifiedSubConfidentialityImpact\":\"NOT_DEFINED\",\"modifiedSubIntegrityImpact\":\"NOT_DEFINED\",\"modifiedSubAvailabilityImpact\":\"NOT_DEFINED\",\"Safety\":\"NOT_DEFINED\",\"Automatable\":\"NOT_DEFINED\",\"Recovery\":\"NOT_DEFINED\",\"valueDensity\":\"NOT_DEFINED\",\"vulnerabilityResponseEffort\":\"NOT_DEFINED\",\"providerUrgency\":\"NOT_DEFINED\"}}],\"cvssMetricV31\":[{\"source\":\"vulnerability@ncsc.ch\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H\",\"baseScore\":7.8,\"baseSeverity\":\"HIGH\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"REQUIRED\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"HIGH\",\"integrityImpact\":\"HIGH\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":1.8,\"impactScore\":5.9}]},\"weaknesses\":[{\"source\":\"vulnerability@ncsc.ch\",\"type\":\"Secondary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-502\"}]}],\"configurations\":[{\"nodes\":[{\"operator\":\"OR\",\"negate\":false,\"cpeMatch\":[{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.41.0\",\"versionEndExcluding\":\"26.1.2\",\"matchCriteriaId\":\"DFA5365C-467B-4706-853F-6790EA3BE8B0\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:26.4.0:beta1:*:*:*:*:*:*\",\"matchCriteriaId\":\"DCBCF62F-7C10-4A5B-BAFD-F306F19A3AC6\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:26.4.0:beta2:*:*:*:*:*:*\",\"matchCriteriaId\":\"2685B96C-478C-4767-9282-B8FD883111D7\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:26.4.0:beta3:*:*:*:*:*:*\",\"matchCriteriaId\":\"E31013A1-4311-49E7-93CF-1E00D79609CD\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:26.4.0:beta4:*:*:*:*:*:*\",\"matchCriteriaId\":\"FA88B146-1C99-44F7-86B5-9F81754270BD\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:zhinst:labone_q:26.4.0:beta5:*:*:*:*:*:*\",\"matchCriteriaId\":\"2DF7C457-282A-4BEF-BDB0-B40A7FC2631C\"}]}]}],\"references\":[{\"url\":\"https://www.zhinst.com/support/security/2026/zi-sa-2026-002/\",\"source\":\"vulnerability@ncsc.ch\",\"tags\":[\"Vendor Advisory\"]}]}}",
    "vulnrichment": {
      "containers": "{\"adp\": [{\"title\": \"CISA ADP Vulnrichment\", \"metrics\": [{\"other\": {\"type\": \"ssvc\", \"content\": {\"id\": \"CVE-2026-7584\", \"role\": \"CISA Coordinator\", \"options\": [{\"Exploitation\": \"none\"}, {\"Automatable\": \"no\"}, {\"Technical Impact\": \"total\"}], \"version\": \"2.0.3\", \"timestamp\": \"2026-05-01T13:26:46.982666Z\"}}}], \"providerMetadata\": {\"orgId\": \"134c704f-9b21-4f2e-91b3-4a467353bcc0\", \"shortName\": \"CISA-ADP\", \"dateUpdated\": \"2026-05-01T13:26:54.611Z\"}}], \"cna\": {\"title\": \"Arbitrary Code Execution via Unsafe Deserialization in LabOne Q\", \"source\": {\"discovery\": \"INTERNAL\"}, \"metrics\": [{\"format\": \"CVSS\", \"cvssV3_1\": {\"scope\": \"UNCHANGED\", \"version\": \"3.1\", \"baseScore\": 7.8, \"attackVector\": \"LOCAL\", \"baseSeverity\": \"HIGH\", \"vectorString\": \"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H\", \"integrityImpact\": \"HIGH\", \"userInteraction\": \"REQUIRED\", \"attackComplexity\": \"LOW\", \"availabilityImpact\": \"HIGH\", \"privilegesRequired\": \"NONE\", \"confidentialityImpact\": \"HIGH\"}, \"scenarios\": [{\"lang\": \"en\", \"value\": \"GENERAL\"}]}, {\"format\": \"CVSS\", \"cvssV4_0\": {\"Safety\": \"NOT_DEFINED\", \"version\": \"4.0\", \"Recovery\": \"NOT_DEFINED\", \"baseScore\": 8.4, \"Automatable\": \"NOT_DEFINED\", \"attackVector\": \"LOCAL\", \"baseSeverity\": \"HIGH\", \"valueDensity\": \"NOT_DEFINED\", \"vectorString\": \"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\", \"exploitMaturity\": \"NOT_DEFINED\", \"providerUrgency\": \"NOT_DEFINED\", \"userInteraction\": \"ACTIVE\", \"attackComplexity\": \"LOW\", \"attackRequirements\": \"NONE\", \"privilegesRequired\": \"NONE\", \"subIntegrityImpact\": \"NONE\", \"vulnIntegrityImpact\": \"HIGH\", \"subAvailabilityImpact\": \"NONE\", \"vulnAvailabilityImpact\": \"HIGH\", \"subConfidentialityImpact\": \"NONE\", \"vulnConfidentialityImpact\": \"HIGH\", \"vulnerabilityResponseEffort\": \"NOT_DEFINED\"}, \"scenarios\": [{\"lang\": \"en\", \"value\": \"GENERAL\"}]}], \"affected\": [{\"vendor\": \"Zurich Instruments\", \"product\": \"LabOne Q\", \"versions\": [{\"status\": \"affected\", \"version\": \"2.41.0\", \"lessThan\": \"26.1.2\", \"versionType\": \"python\"}, {\"status\": \"affected\", \"version\": \"26.4.0b1\", \"versionType\": \"python\", \"lessThanOrEqual\": \"26.4.0b5\"}], \"packageName\": \"laboneq\", \"collectionURL\": \"https://pypi.org/project/laboneq/\", \"defaultStatus\": \"unaffected\"}], \"solutions\": [{\"lang\": \"en\", \"value\": \"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`.\", \"supportingMedia\": [{\"type\": \"text/html\", \"value\": \"\u003cp\u003eUpdate 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`.\u003c/p\u003e\", \"base64\": false}]}], \"references\": [{\"url\": \"https://www.zhinst.com/support/security/2026/zi-sa-2026-002/\", \"name\": \"ZI-SA-2026-002\", \"tags\": [\"vendor-advisory\"]}], \"workarounds\": [{\"lang\": \"en\", \"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.\", \"supportingMedia\": [{\"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\", \"base64\": false}]}], \"x_generator\": {\"engine\": \"Vulnogram 1.0.2\"}, \"descriptions\": [{\"lang\": \"en\", \"value\": \"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\u0027s deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.\", \"supportingMedia\": [{\"type\": \"text/html\", \"value\": \"\u003cp\u003eThe 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\u0027s deserialization functions, for example a compromised experiment file shared for collaboration or support purposes.\u003c/p\u003e\", \"base64\": false}]}], \"problemTypes\": [{\"descriptions\": [{\"lang\": \"en\", \"type\": \"CWE\", \"cweId\": \"CWE-502\", \"description\": \"CWE-502 Deserialization of Untrusted Data\"}]}], \"providerMetadata\": {\"orgId\": \"455daabc-a392-441d-aa46-37d35189897c\", \"shortName\": \"NCSC.ch\", \"dateUpdated\": \"2026-05-01T07:21:18.781Z\"}}}",
      "cveMetadata": "{\"cveId\": \"CVE-2026-7584\", \"state\": \"PUBLISHED\", \"dateUpdated\": \"2026-05-01T13:26:59.075Z\", \"dateReserved\": \"2026-05-01T07:14:23.592Z\", \"assignerOrgId\": \"455daabc-a392-441d-aa46-37d35189897c\", \"datePublished\": \"2026-05-01T07:21:18.781Z\", \"assignerShortName\": \"NCSC.ch\"}",
      "dataType": "CVE_RECORD",
      "dataVersion": "5.2"
    }
  }
}


Log in or create an account to share your comment.




Tags
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.