Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is due to a lack of proper input validation during the pickle deserialization process within the BaseCard.load function in the recipes/cards/init.py file. This vulnerability allows an attacker to execute arbitrary code o...

Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is caused due to improper handling of serialized data in the loadpyfunc function within mlflow/pyfunc/model.py. This flaw allows an attacker to inject a malicious pickle object into a PyFunc model file, which results in...

Code Injection

mlflow is vulnerable to Code Injection. The vulnerability is caused due to improper input validation in the runentrypoint function within the projects/backend/local.py file. This vulnerability allows an attacker to execute arbitrary code on the victim's system by submitting a maliciously crafted...

Undefined Behavior

mlflow is vulnerable to Undefined Behavior. The vulnerability is due to inadequate validation of model names, which allows an attacker to create multiple models with the same name, leading to potential Denial of Service DoS and data model poisoning...

Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is due to inadequate input validation in the loadcustomobjects function within mlflow/tensorflow/init.py, which allows attackers to execute arbitrary code by injecting a malicious pickle object into the Tensorflow model...

Arbitrary File Write

mlflow is vulnerable to Arbitrary File Write. The vulnerability is due to improper santization within the mlflow.data.httpdatasetsource.py module, when fetching data over HTTP. The Content-Disposition header is used directly to construct the path where the file is saved to, which allows an attack...

Deserialization Of Untrusted Data

mlflow is vulnerable to Deserialization of Untrusted Data. The vulnerability is caused by a lack of validation in the loadfrompickle function in the mlflow/langchain/utils.py file, allowing an attacker to execute arbitrary code on the victim's system through a malicious Langchain AgentExecutor...

BIT-MLFLOW-2024-37052

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with...

BIT-MLFLOW-2024-37053

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with...

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...

BIT-MLFLOW-2024-37055

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with...

BIT-MLFLOW-2024-37056

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with...

BIT-MLFLOW-2024-37058

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with...

BIT-MLFLOW-2024-37059

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with...

BIT-MLFLOW-2024-37060

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run...

BIT-MLFLOW-2024-37061

Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run...

a2 (>=0.1.0 <=0.3.17), agentos (>=0.0.5 <=0.0.7) +158 more potentially affected by CVE-2024-2928 via mlflow (>=0.8.2 <=2.11.1)

mlflow PYPI version =0.8.2, =0.1.0, =0.0.5, =0.1.2, =1.0.18.2, =0.0.1, =1.0.41, =1.4.0, =0.2.5, =3.0.0, =0.1.0, =0.3.5, =0.8.0, =1.2.0, =1.9.30 and more Source cves: CVE-2024-2928 Source advisory: OSV:GHSA-J46Q-5PXX-8VMW...

a2 (>=0.1.0 <=0.3.17), agentos (>=0.0.5 <=0.0.7) +158 more potentially affected by CVE-2024-3099 via mlflow (>=0.8.2 <=2.11.1)

mlflow PYPI version =0.8.2, =0.1.0, =0.0.5, =0.1.2, =1.0.18.2, =0.0.1, =1.0.41, =1.4.0, =0.2.5, =3.0.0, =0.1.0, =0.3.5, =0.8.0, =1.2.0, =1.9.30 and more Source cves: CVE-2024-3099 Source advisory: OSV:GHSA-8F8Q-Q2J7-7J2M...

GHSA-8F8Q-Q2J7-7J2M Undefined Behavior in mlflow

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service DoS as an authenticated user might not be able to use the intended model, as it will open a different model each time...

Undefined Behavior in mlflow

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service DoS as an authenticated user might not be able to use the intended model, as it will open a different model each time...