Vulnerability-Lookup

Vulnerability from bitnami_vulndb

Published

2024-03-31 18:21

Modified

2025-05-20 10:02

Summary

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

Details

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields.

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "mlflow",
        "purl": "pkg:bitnami/mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.10.0"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2024-27133"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*",
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:python:*:*"
    ],
    "severity": "Critical"
  },
  "details": "Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields.",
  "id": "BIT-mlflow-2024-27133",
  "modified": "2025-05-20T10:02:07.006Z",
  "published": "2024-03-31T18:21:40.530Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/pull/10893"
    },
    {
      "type": "WEB",
      "url": "https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-27133"
    }
  ],
  "schema_version": "1.5.0",
  "summary": "Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset."
}

CVE-2024-27133 (GCVE-0-2024-27133)

Vulnerability from cvelistv5 – Published: 2024-02-23 22:00 – Updated: 2024-08-22 18:01

Title

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

Summary

Severity ?

7.5 (High)


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

CWE

CWE-79 - Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')

Assigner

JFROG

References

URL

Tags

	https://research.jfrog.com/vulnerabilities/mlflow…
	https://github.com/mlflow/mlflow/pull/10893

Impacted products

	Vendor	Product	Version
			Affected: 0 , ≤ 2.9.2 (python)

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-02T00:27:59.107Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/"
          },
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/mlflow/mlflow/pull/10893"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "affected": [
          {
            "cpes": [
              "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*"
            ],
            "defaultStatus": "unknown",
            "product": "mlflow",
            "vendor": "lfprojects",
            "versions": [
              {
                "lessThanOrEqual": "2.9.2",
                "status": "affected",
                "version": "0",
                "versionType": "custom"
              }
            ]
          }
        ],
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2024-27133",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2024-02-27T15:45:19.756510Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2024-08-22T18:01:49.002Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "collectionURL": "https://pypi.org/project/pip",
          "packageName": "mlflow",
          "versions": [
            {
              "lessThanOrEqual": "2.9.2",
              "status": "affected",
              "version": "0",
              "versionType": "python"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "supportingMedia": [
            {
              "base64": false,
              "type": "text/html",
              "value": "Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields."
            }
          ],
          "value": "Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "format": "CVSS",
          "scenarios": [
            {
              "lang": "en",
              "value": "GENERAL"
            }
          ]
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-79",
              "description": "CWE-79 Improper Neutralization of Input During Web Page Generation (\u0027Cross-site Scripting\u0027)",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2024-02-23T22:00:33.124Z",
        "orgId": "48a46f29-ae42-4e1d-90dd-c1676c1e5e6d",
        "shortName": "JFROG"
      },
      "references": [
        {
          "url": "https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/"
        },
        {
          "url": "https://github.com/mlflow/mlflow/pull/10893"
        }
      ],
      "source": {
        "discovery": "EXTERNAL"
      },
      "title": "Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset."
    }
  },
  "cveMetadata": {
    "assignerOrgId": "48a46f29-ae42-4e1d-90dd-c1676c1e5e6d",
    "assignerShortName": "JFROG",
    "cveId": "CVE-2024-27133",
    "datePublished": "2024-02-23T22:00:33.124Z",
    "dateReserved": "2024-02-20T11:10:51.335Z",
    "dateUpdated": "2024-08-22T18:01:49.002Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date

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.

Action not permitted

Vulnerability from bitnami_vulndb

CVE-2024-27133 (GCVE-0-2024-27133)

Tags

Sightings

Nomenclature