CVE-2024-37054-PoC

Description Proof of concept for CVE-2024-37054, a critical deserialization vulnerability in MLflow versions 0.9.0 to 2.14.1. When a malicious pyfunc model is loaded via mlflow.pyfunc.loadmodel, the pickled payload executes arbitrary code. Files exploit.py — full exploit: register, train, upload,...

CVE-2024-37054-PoC

MLflow Unsafe Deserialization CVE-2024-37054 Overview This repository documents research into CVE-2024-37054 , an unsafe deserialization issue affecting MLflow PyFunc model handling. In affected MLflow versions, loading an untrusted PyFunc model can cause Python deserialization logic to process a...

PYSEC-2026-1651 MLFlow unsafe deserialization

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is caused by a lack of proper validation of untrusted data in the loadmodel function within the pmdarima/init.py file, allowing an attacker to execute arbitrary code by injecting a malicious pickle object into a PyFunc...

BIT-MLFLOW-2024-37054

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is caused due to inadequate input validation in the loadmodel function within mlflow/pytorch/init.py. This allows an attacker to execute arbitrary code on the victim's system by injecting a malicious pickle object into a...

GHSA-GHV6-9R9J-WH4J MLFlow unsafe deserialization

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

MLFlow unsafe deserialization

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

CVE-2024-37054

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

CVE-2024-37054

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

CVE-2024-37054

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

CVE-2024-37054

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

CVE-2024-37054: Deserialization of Untrusted Data

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with...

PT-2024-27269

Name of the Vulnerable Software and Affected Versions MLflow platform versions 0.9.0 and newer Description The issue allows deserialization of untrusted data, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user's system when interacted with. Recommendations For MLflo...