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MLflow Server-Side Request Forgery (SSRF)

Critical severity GitHub Reviewed Published Dec 20, 2023 to the GitHub Advisory Database • Updated Jan 2, 2024

Package

pip mlflow (pip)

Affected versions

< 2.9.2

Patched versions

2.9.2

Description

A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abused to get a remote code execution on the victim machine.

References

Published by the National Vulnerability Database Dec 20, 2023
Published to the GitHub Advisory Database Dec 20, 2023
Reviewed Dec 20, 2023
Last updated Jan 2, 2024

Severity

Critical

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
None
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:N/S:U/C:H/I:H/A:H

EPSS score

0.264%
(66th percentile)

Weaknesses

CVE ID

CVE-2023-6974

GHSA ID

GHSA-59v3-898r-qwhj

Source code

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