CVE intelligence and bounded remediation

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

Critical CVSS 9.6

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.

Severity
Critical
CVSS
9.6 (3.1)
Published
2024-02-23
CISA KEV
Not currently listed
Ecosystem
software/application
Weaknesses
CWE-79

Affected products

  • lfprojects / mlflow

Matched remediation archetype

Cross-site scripting and unsafe browser output

This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.

Check exposure

  • Trace reflected, stored, and DOM-derived untrusted values into HTML, attributes, URLs, styles, scripts, and client-side template sinks.
  • Identify affected origins, authenticated user roles, sensitive browser capabilities, and where content is shared across tenants.
  • Review framework escaping, rich-text sanitization, legacy templates, and client-side rendering paths.

Remediate safely

  • Use context-aware framework output encoding and safe DOM APIs; keep untrusted data out of executable contexts.
  • Sanitize intentionally supported markup with a maintained allowlist policy and validate URLs and attributes separately.
  • Update affected rendering components and add tests for every output context using inert sentinel markup.

Authoritative sources

Complete CVE record and remediation plan

The detailed catalog view below loads this exact record, its source evidence, and the full seven-phase agentic change plan.