Deserialization Of Untrusted Data

2024-06-1704:59:38

Veracode Vulnerability Database

sca.analysiscenter.veracode.com

mlflow

vulnerability

deserialization

untrusted data

improper handling

sklearn package

arbitrary code execution

model file

injection

pickle object

CVSS3

8.8

Attack Vector

NETWORK

Attack Complexity

LOW

Privileges Required

NONE

User Interaction

REQUIRED

Scope

UNCHANGED

Confidentiality Impact

HIGH

Integrity Impact

HIGH

Availability Impact

HIGH

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

AI Score

7.3

Confidence

High

EPSS

Percentile

9.0%

JSON

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is due to improper handling of untrusted data in the _load_model_from_local_file function within the sklearn/__init__.py. The vulnerability allows an attacker to inject a malicious pickle object into a model file on upload, which is then deserialized when the model is loaded, resulting in arbitrary code execution within the victims machine.