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...

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...

Local File Inclusion in mlflow

A Local File Inclusion LFI vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. This vulnerability arises from the application's failure to properly validate URI fragments for directory traversal sequences such as '../'. An attacker can...

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...

Remote code execution in mlflow

A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command 'Command Injection' within the mlflow.data.httpdatasetsource.py module. Specifically, when loading a dataset from a source URL with an HTTP...

CVE-2024-3099

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...

CVE-2024-3099

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...

CVE-2024-2928

A Local File Inclusion LFI vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. This vulnerability arises from the application's failure to properly validate URI fragments for directory traversal sequences such as '../'. An attacker can...