CVE-2024-37057: versions of the MLflow platform running · Deserialization of Untrusted Data Can Occur
Affects mlflowSource GCVE Database
Not known to be exploited.
Data as of 26 Oct 2026, 06:26 UTC
Rated by Threat Radar · No CVSS score from NVD or the CNA
- EPSS
- 0.6% chance of exploitation in 30 days – higher than 49% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Exploitation evidence
Disclosed: evidence found.
CVE published 10 Jun 2024
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
Bitnamimlflowpkg:bitnami/mlflowOther threats | Affected
| Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
Until an official fix is available, avoid interacting with untrusted Tensorflow models uploaded to MLflow. Monitor vendor channels for updates and apply patches promptly once released.
Description
A deserialization vulnerability exists in the MLflow platform versions 2.0.0 through 2.14.1. This flaw allows a maliciously crafted Tensorflow model uploaded to the platform to execute arbitrary code on the system when interacted with by an end user. The vulnerability is classified as high severity due to the potential for remote code execution.