Vulnerability-Lookup

FKIE_CVE-2024-27134

Vulnerability from fkie_nvd - Published: 2024-11-25 14:15 - Updated: 2026-06-17 07:19

Severity

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

Summary

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called.

References

	URL	Tags
	reefs@jfrog.com	https://github.com/mlflow/mlflow/pull/10874	Issue Tracking, Patch

Impacted products

	Vendor	Product	Version
	lfprojects	mlflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "collectionURL": "https://pypi.python.org",
          "defaultStatus": "unaffected",
          "packageName": "mlflow",
          "versions": [
            {
              "lessThan": "2.16.0",
              "status": "affected",
              "version": "0",
              "versionType": "python"
            }
          ]
        }
      ],
      "source": "reefs@jfrog.com"
    },
    {
      "affectedData": [
        {
          "cpes": [
            "cpe:2.3:a:lfprojects:mlflow:-:*:*:*:*:*:*:*"
          ],
          "defaultStatus": "unknown",
          "product": "mlflow",
          "vendor": "lfprojects",
          "versions": [
            {
              "lessThan": "2.16.0",
              "status": "affected",
              "version": "0",
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          ]
        }
      ],
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "B6FD49A4-6E91-44B8-AB67-EC2626F98335",
              "versionEndExcluding": "2.16.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called."
    },
    {
      "lang": "es",
      "value": "Los permisos de directorio excesivos en MLflow provocan una escalada de privilegios locales cuando se utiliza spark_udf. Un atacante local puede aprovechar este comportamiento para obtener permisos elevados mediante un ataque ToCToU. El problema solo es relevante cuando se llama a la API spark_udf() de MLflow."
    }
  ],
  "id": "CVE-2024-27134",
  "lastModified": "2026-06-17T07:19:13.537",
  "metrics": {
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        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 7.0,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:L/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.0,
        "impactScore": 5.9,
        "source": "reefs@jfrog.com",
        "type": "Secondary"
      },
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          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 7.0,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.0,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
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          "role": "CISA Coordinator",
          "timestamp": "2024-11-25T14:23:17.524784Z",
          "version": "2.0.3"
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  },
  "published": "2024-11-25T14:15:06.867",
  "references": [
    {
      "source": "reefs@jfrog.com",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/mlflow/mlflow/pull/10874"
    }
  ],
  "sourceIdentifier": "reefs@jfrog.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-276"
        },
        {
          "lang": "en",
          "value": "CWE-367"
        }
      ],
      "source": "reefs@jfrog.com",
      "type": "Secondary"
    }
  ]
}

CVE-2024-27134 (GCVE-0-2024-27134)

Vulnerability from cvelistv5 – Published: 2024-11-25 13:48 – Updated: 2024-11-25 14:23

Title

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf

Summary

Severity

7 (High)


                        
                          CVSS:3.1/AV:L/AC:H/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 2024-11-25 14:23 UTC

CWE

CWE-367 - Time-of-check Time-of-use (TOCTOU) Race Condition
CWE-276 - Incorrect Default Permissions

Assigner

JFROG mitre root

References

1 reference

URL	Tags
https://github.com/mlflow/mlflow/pull/10874	patch

Impacted products

2 products

Vendor	Product	Version
		Affected: 0 , < 2.16.0 (python)
lfprojects	mlflow	Affected: 0 , < 2.16.0 (custom) cpe:2.3:a:lfprojects:mlflow:-:::::::*

Show details on NVD website

JSON

To clipboard

{
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      {
        "affected": [
          {
            "cpes": [
              "cpe:2.3:a:lfprojects:mlflow:-:*:*:*:*:*:*:*"
            ],
            "defaultStatus": "unknown",
            "product": "mlflow",
            "vendor": "lfprojects",
            "versions": [
              {
                "lessThan": "2.16.0",
                "status": "affected",
                "version": "0",
                "versionType": "custom"
              }
            ]
          }
        ],
        "metrics": [
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            "other": {
              "content": {
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                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2024-11-25T14:23:17.524784Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
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        ],
        "providerMetadata": {
          "dateUpdated": "2024-11-25T14:23:59.324Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
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    "cna": {
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          "defaultStatus": "unaffected",
          "packageName": "mlflow",
          "versions": [
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              "version": "0",
              "versionType": "python"
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      "descriptions": [
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            "attackVector": "LOCAL",
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      "problemTypes": [
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            {
              "cweId": "CWE-367",
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        },
        {
          "descriptions": [
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    "dateReserved": "2024-02-20T11:10:51.335Z",
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    "state": "PUBLISHED"
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}

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.

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.