Vulnerability-Lookup

CVE-2024-37054 (GCVE-0-2024-37054)

Vulnerability from cvelistv5 – Published: 2024-06-04 12:00 – Updated: 2024-08-02 03:43

Summary

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with.

Severity

8.8 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2024-06-06 15:01 UTC

CWE

CWE-502 - Deserialization of Untrusted Data

Assigner

HiddenLayer CNA under the mitre root

CNA scorecard C 70/100 over 16 records in the last 180 days details

References

1 reference

URL	Tags
https://hiddenlayer.com/sai-security-advisory/mlf…

Impacted products

2 products

Vendor	Product	Version	CPE status
MLflow	MLflow	Affected: 0.9.0 , ≤ * (semver)	guessed
lfprojects	mlflow	Affected: 0.9.0 , ≤ * (custom) cpe:2.3:a:lfprojects:mlflow:0.9.0:::::::*

Show details on NVD website

JSON

To clipboard

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bit-mlflow-2024-37054

Vulnerability from bitnami_vulndb

Published

2024-06-08 07:26

Modified

2026-09-08 08:34

Summary

Details

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "mlflow",
        "purl": "pkg:bitnami/mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.9.0"
            },
            {
              "fixed": "2.14.2"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2024-37054"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:python:*:*"
    ],
    "severity": "High"
  },
  "details": "Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user\u2019s system when interacted with.",
  "id": "BIT-mlflow-2024-37054",
  "modified": "2026-09-08T08:34:36.952Z",
  "published": "2024-06-08T07:26:40.212Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://hiddenlayer.com/sai-security-advisory/mlflow-june2024"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-37054"
    }
  ],
  "schema_version": "1.5.0"
}

FKIE_CVE-2024-37054

Vulnerability from fkie_nvd - Published: 2024-06-04 12:15 - Updated: 2026-06-17 07:37

Severity

8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Summary

References

	URL	Tags
	6f8de1f0-f67e-45a6-b68f-98777fdb759c	https://hiddenlayer.com/sai-security-advisory/mlflow-june2024	Exploit, Third Party Advisory
	af854a3a-2127-422b-91ae-364da2661108	https://hiddenlayer.com/sai-security-advisory/mlflow-june2024	Exploit, Third Party Advisory

Impacted products

	Vendor	Product	Version
	lfprojects	mlflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
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          "defaultStatus": "unaffected",
          "packageName": "mlflow",
          "product": "MLflow",
          "repo": "https://github.com/mlflow/mlflow",
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    },
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    {
      "lang": "es",
      "value": "La deserializaci\u00f3n de datos que no son de confianza puede ocurrir en versiones de la plataforma MLflow que ejecutan la versi\u00f3n 0.9.0 o posterior, lo que permite que un modelo PyFunc cargado maliciosamente ejecute c\u00f3digo arbitrario en el sistema de un usuario final cuando interact\u00faa con \u00e9l."
    }
  ],
  "id": "CVE-2024-37054",
  "lastModified": "2026-06-17T07:37:41.117",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2024-37054",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2024-06-06T15:01:38.249209Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2024-06-04T12:15:11.190",
  "references": [
    {
      "source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://hiddenlayer.com/sai-security-advisory/mlflow-june2024"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://hiddenlayer.com/sai-security-advisory/mlflow-june2024"
    }
  ],
  "sourceIdentifier": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-502"
        }
      ],
      "source": "6f8de1f0-f67e-45a6-b68f-98777fdb759c",
      "type": "Secondary"
    },
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-502"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}

GHSA-GHV6-9R9J-WH4J

Vulnerability from github – Published: 2024-06-04 12:31 – Updated: 2024-06-21 14:25

Summary

MLFlow unsafe deserialization

Details

Severity

8.8 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.9.0"
            },
            {
              "last_affected": "2.14.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2024-37054"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2024-06-05T13:17:58Z",
    "nvd_published_at": "2024-06-04T12:15:11Z",
    "severity": "HIGH"
  },
  "details": "Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user\u2019s system when interacted with.",
  "id": "GHSA-ghv6-9r9j-wh4j",
  "modified": "2024-06-21T14:25:31Z",
  "published": "2024-06-04T12:31:04Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-37054"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mlflow/mlflow"
    },
    {
      "type": "WEB",
      "url": "https://hiddenlayer.com/sai-security-advisory/mlflow-june2024"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "MLFlow unsafe deserialization"
}

PYSEC-2026-1651

Vulnerability from pysec - Published: 2026-07-07 11:45 - Updated: 2026-07-07 17:24

Details

Severity

8.8 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Impacted products

Name	purl
mlflow	pkg:pypi/mlflow

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "mlflow",
        "purl": "pkg:pypi/mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.9.0"
            },
            {
              "last_affected": "2.14.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.9.0",
        "0.9.0.1",
        "0.9.1",
        "1.0.0",
        "1.1.0",
        "1.1.1.dev0",
        "1.10.0",
        "1.11.0",
        "1.12.0",
        "1.12.1",
        "1.13",
        "1.13.1",
        "1.14.0",
        "1.14.1",
        "1.15.0",
        "1.16.0",
        "1.17.0",
        "1.18.0",
        "1.19.0",
        "1.2.0",
        "1.20.0",
        "1.20.1",
        "1.20.2",
        "1.21.0",
        "1.22.0",
        "1.23.0",
        "1.23.1",
        "1.24.0",
        "1.25.0",
        "1.25.1",
        "1.26.0",
        "1.26.1",
        "1.27.0",
        "1.28.0",
        "1.29.0",
        "1.3.0",
        "1.30.0",
        "1.30.1",
        "1.4.0",
        "1.5.0",
        "1.6.0",
        "1.7.0",
        "1.7.1",
        "1.7.2",
        "1.8.0",
        "1.9.0",
        "1.9.1",
        "2.0.0",
        "2.0.0rc0",
        "2.0.1",
        "2.1.0",
        "2.1.1",
        "2.10.0",
        "2.10.1",
        "2.10.2",
        "2.11.0",
        "2.11.1",
        "2.11.2",
        "2.11.3",
        "2.11.4",
        "2.12.0",
        "2.12.1",
        "2.12.2",
        "2.13.0",
        "2.13.1",
        "2.13.2",
        "2.14.0",
        "2.14.0rc0",
        "2.14.1",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.4.0",
        "2.4.1",
        "2.4.2",
        "2.5.0",
        "2.6.0",
        "2.7.0",
        "2.7.1",
        "2.8.0",
        "2.8.1",
        "2.9.0",
        "2.9.1",
        "2.9.2"
      ]
    }
  ],
  "aliases": [
    "CVE-2024-37054",
    "GHSA-ghv6-9r9j-wh4j"
  ],
  "details": "Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user\u2019s system when interacted with.",
  "id": "PYSEC-2026-1651",
  "modified": "2026-07-07T17:24:42.712517Z",
  "published": "2026-07-07T11:45:44.663984Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-37054"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mlflow/mlflow"
    },
    {
      "type": "WEB",
      "url": "https://hiddenlayer.com/sai-security-advisory/mlflow-june2024"
    },
    {
      "type": "PACKAGE",
      "url": "https://pypi.org/project/mlflow"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-ghv6-9r9j-wh4j"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "MLFlow unsafe deserialization"
}

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.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.

Action not permitted

CVE-2024-37054 (GCVE-0-2024-37054)

bit-mlflow-2024-37054

Vulnerability from bitnami_vulndb

FKIE_CVE-2024-37054

GHSA-GHV6-9R9J-WH4J

PYSEC-2026-1651

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