Vulnerability from bitnami_vulndb
Published
2026-04-21 12:08
Modified
2026-04-21 12:33
Summary
Stored XSS via unsafe YAML parsing in MLflow
Details

MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim.

This issue affects MLflow version through 3.10.1


{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "mlflow",
        "purl": "pkg:bitnami/mlflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.11.1"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:P/VC:N/VI:L/VA:N/SC:L/SI:L/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",
          "type": "CVSS_V4"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-33865"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:lfprojects:mlflow:*:*:*:*:*:*:*:*"
    ],
    "severity": "Medium"
  },
  "details": "MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. \n\nThis issue affects MLflow version through 3.10.1",
  "id": "BIT-mlflow-2026-33865",
  "modified": "2026-04-21T12:33:30.555Z",
  "published": "2026-04-21T12:08:45.926Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://afine.com/blogs/attacking-mlflow-how-ml-artifacts-become-attack-vectors"
    },
    {
      "type": "WEB",
      "url": "https://cert.pl/en/posts/2026/04/CVE-2026-33865/"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mlflow/mlflow/pull/21435"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-33865"
    }
  ],
  "schema_version": "1.6.2",
  "summary": "Stored XSS via unsafe YAML parsing in MLflow"
}


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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.


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