Home/CVE/Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads
CVE

CVE-2024-27133

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

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.

HIGH · CVSS 7.5 EPSS 0.00204
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  • Public exploit or PoC is available
  • CVSS base score ≥ 7.0
Sigma rules0 YARA rules0
⬡

Weakness Classification

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

1
lfprojects mlflow<= 2.9.2
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Affected Packages

1
Language-ecosystem packages (from OSV) tied to this CVE, with the version that fixes it - the dependency-level detail NVD doesn’t carry.
PyPI mlflow CRITICAL fixed in 2.10.0
⚠

Public Exploits & PoCs

1
pocresearch.jfrog.com · mlflow-untrusted-dataset-xss-jfsa-2024-000631932research.jfrog.com
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Scoring & Timeline

7.5
HIGH · CVSS v3.1 · reefs@jfrog.com
View on NVD
Attack Vector
Network Adjacent Local Physical
Attack Complexity
Low High
Privileges Required
None Low High
User Interaction
None Required
Scope
Unchanged Changed
Confidentiality
None Low High
Integrity
None Low High
Availability
None Low High
Published to NVD23 Feb 2024 · 10:15 PM
CVSS VectorCVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
SSVC triage · cisa-vulnrichment
Exploitation
poc
Automatable
no
Technical impact
partial
SSVC asks the questions that actually drive patch urgency: is it being exploited, can attacks be automated, and how total is the impact.
🔗

References & Sources

1
Source URLs (vendor pages, mailing lists, write-ups). Exploit/PoC links are in their own section above to avoid duplication.
https://github.com/mlflow/mlflow/pull/10893Issue TrackingPatch
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