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MLFlow unsafe deserialization

High severity GitHub Reviewed Published Jun 4, 2024 to the GitHub Advisory Database • Updated Jun 21, 2024

Package

pip mlflow (pip)

Affected versions

>= 2.0.0rc0, <= 2.14.1

Patched versions

None

Description

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.

References

Published by the National Vulnerability Database Jun 4, 2024
Published to the GitHub Advisory Database Jun 4, 2024
Reviewed Jun 5, 2024
Last updated Jun 21, 2024

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
None
User interaction
Required
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

EPSS score

0.043%
(10th percentile)

Weaknesses

CVE ID

CVE-2024-37057

GHSA ID

GHSA-j8mg-pqc5-x9gj

Source code

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