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CRITICAL severity

MLFlow Cross-site Scripting vulnerability leads to client-side Remote Code ExecutionGHSA-3v79-q7ph-j75h

CRITICALFix: mlflow/mlflow#10893

GHSA-3v79-q7ph-j75h is a critical-severity (CVSS 9.6) Cross-site Scripting (XSS) vulnerability in mlflow. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. A fix is available for mlflow — see the affected versions and patch details below.

Also known asBIT-mlflow-2024-27133CVE-2024-27133PYSEC-2024-241
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
1 known
Exploitation data as of Nov 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.

Exploitation and automatability from CISA’s SSVC triage for GHSA-3v79-q7ph-j75h.

EPSS Exploitation Probability

via FIRST.org ↗
0.7%probability of exploitation in next 30 days
Lower Risk-0.00%
Lower risk than most CVEs52th percentile — riskier than 52% of all scored CVEsHighest risk
0.00%0.39%0.77%1.16%0.2%0.7%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

1 pkg affected
🐍mlflow

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPImlflowall versions2.10.0pip install --upgrade 'mlflow==2.10.0'

Affected Products

1 product · 1 configurations
Application
mlflowlfprojects
≤ 2.9.2
range
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Insufficient sanitization in MLflow leads to XSS when running a recipe t…

research.jfrog.comNVDJan 2025

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for mlflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update mlflow to 2.10.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-3v79-q7ph-j75h is resolved across your whole dependency graph.

  3. Workarounds

    Escape or sanitise the affected output on the server side rather than relying on client-side filtering, and add a Content-Security-Policy that blocks inline script execution so injected markup cannot run even if it reaches the page.

Frequently Asked Questions

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.
O3 Security · Impact-Aware SCA

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MLFlow Cross-site Scripting vulnerability leads to…