VulnerabilityAnalyzed
CVE-2024-37052
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.
HIGH 8.8EPSS 0.62%
Does this matter?
High impact if exploited, but EPSS currently rates exploitation as unlikely (0.62%). Schedule it in the normal patch cycle and watch for a rise in EPSS or a public exploit.
Description
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.
- CVSS 3.1
- 8.8 HIGHCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
- EPSS
- 0.62% probability · 48th percentile
- CISA KEV
- Not listed
- Weakness
- CWE-502
- Affected
- lfprojects/mlflow
- Source
- 6f8de1f0-f67e-45a6-b68f-98777fdb759c
References
- https://hiddenlayer.com/sai-security-advisory/mlflow-june2024Exploit, Third Party Advisory
- https://hiddenlayer.com/sai-security-advisory/mlflow-june2024Exploit, Third Party Advisory
Source: NVD record, EPSS from FIRST.org, KEV from CISA. Refreshed daily.