VulnerabilityAnalyzed
CVE-2024-27133
Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.
CRITICAL 9.6EPSS 0.66%
Does this matter?
High impact if exploited, but EPSS currently rates exploitation as unlikely (0.66%). Schedule it in the normal patch cycle and watch for a rise in EPSS or a public exploit.
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.
- CVSS 3.1
- 9.6 CRITICALCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H
- EPSS
- 0.66% probability · 49th percentile
- CISA KEV
- Not listed
- Weakness
- CWE-79
- Affected
- lfprojects/mlflow
- Source
- reefs@jfrog.com
References
- https://github.com/mlflow/mlflow/pull/10893Issue Tracking, Patch
- https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/Exploit, Third Party Advisory
- https://github.com/mlflow/mlflow/pull/10893Issue Tracking, Patch
- https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/Exploit, Third Party Advisory
Source: NVD record, EPSS from FIRST.org, KEV from CISA. Refreshed daily.