CVE-2024-37053 — Deserialization of untrusted data can occur in versions of the MLflow platform r

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

01What is this vulnerability?

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

02Affected products

Vendor	Product	Versions
MLflow	MLflow	1.1.0

03Active exploitation status

Not currently listed on the CISA Known Exploited Vulnerabilities (KEV) catalog. EPSS is the best forward-looking signal — see the EPSS row above.

04Recommended remediation

• Patch to a fixed version listed in the vendor advisory (see references below).
• Mitigate with WAF rules, network egress filters, or feature flags where the patch is not yet available.
• Hunt historical logs for exploitation indicators — see Detection signatures below.

05Technical details

For the full vendor write-up, exploit chains, and reference implementations, see the references list in section 09.

06Detection signatures

Open the Sigma generator with a pre-filled prompt for this CVE to draft a starting detection in your stack of choice:

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07Related CVEs

No directly-cited follow-up CVEs in the KB record for this advisory. The references list in section 09 carries the vendor cross-references.

08Timeline

• Published: 2024-06-04T12:00:15.162Z
• Last modified: 2024-08-02T03:43:50.998Z

09References

• hiddenlayer.com — https://hiddenlayer.com/sai-security-advisory/mlflow-june2024