mlflow 3.14.0

Direct Vulnerabilities

Known vulnerabilities in the mlflow package. This does not include vulnerabilities belonging to this package’s dependencies.

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Vulnerability	Vulnerable Version
H Direct Request ('Forced Browsing') mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Direct Request ('Forced Browsing') in the Gateway API endpoints due to insufficient authorization checks. An attacker can access sensitive information, including secrets, endpoint configurations, and proprietary model definitions, by sending authenticated requests to the affected endpoints. Note: This is only exploitable if the deployment is configured with basic authentication, regardless of the user's specific permissions. How to fix Direct Request ('Forced Browsing')? There is no fixed version for `mlflow`.	[0,)
L Use of Weak Hash mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Use of Weak Hash in the `mlflow.data.digest_utils` function. An attacker can compromise data integrity or cause unexpected behavior by exploiting the use of a weak hash algorithm during dataset digest computation. How to fix Use of Weak Hash? There is no fixed version for `mlflow`.	[0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `load` function in the `BaseCard` class within the `recipes/cards/__init__.py` file. An attacker can execute arbitrary code on the target system by creating an MLProject Recipe containing a malicious pickle file (e.g. `pickle.pkl`) and a python script that calls `BaseCard.load(pickle.pkl)`. The pickle file will be deserialized when the project is run. Note: If you are not running MLflow on a publicly accessible server, this vulnerability won't apply to you. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[1.27.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_model` function in the `mlflow/pytorch/__init__.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[0.5.0,)
H Improper Control of Generation of Code ('Code Injection') mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Improper Control of Generation of Code ('Code Injection') via the `_run_entry_point` function in the `projects/backend/local.py` file. An attacker can execute arbitrary code on the victim's system by submitting a maliciously crafted MLproject file. How to fix Improper Control of Generation of Code ('Code Injection')? There is no fixed version for `mlflow`.	[1.11.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_from_pickle` function in the `mlflow/langchain/utils.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[2.5.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_custom_objects` function in the `mlflow/tensorflow/__init__.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[2.0.0rc0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_model` function in the `mlflow/lightgbm/__init__.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[1.23.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_model` function in the `pmdarima/__init__.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[1.24.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_model_from_local_file` function in the `sklearn/__init__.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model, which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[1.1.0,)
H Deserialization of Untrusted Data mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `_load_pyfunc` function in the `mlflow/pyfunc/model.py` file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded. How to fix Deserialization of Untrusted Data? There is no fixed version for `mlflow`.	[0.9.0,)

Vulnerability

Vulnerable Version

Direct Request ('Forced Browsing')

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Direct Request ('Forced Browsing') in the Gateway API endpoints due to insufficient authorization checks. An attacker can access sensitive information, including secrets, endpoint configurations, and proprietary model definitions, by sending authenticated requests to the affected endpoints.

Note:

This is only exploitable if the deployment is configured with basic authentication, regardless of the user's specific permissions.

How to fix Direct Request ('Forced Browsing')?

There is no fixed version for mlflow.

[0,)

Use of Weak Hash

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Use of Weak Hash in the mlflow.data.digest_utils function. An attacker can compromise data integrity or cause unexpected behavior by exploiting the use of a weak hash algorithm during dataset digest computation.

How to fix Use of Weak Hash?

There is no fixed version for mlflow.

[0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the load function in the BaseCard class within the recipes/cards/__init__.py file. An attacker can execute arbitrary code on the target system by creating an MLProject Recipe containing a malicious pickle file (e.g. pickle.pkl) and a python script that calls BaseCard.load(pickle.pkl). The pickle file will be deserialized when the project is run.

Note:

If you are not running MLflow on a publicly accessible server, this vulnerability won't apply to you.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[1.27.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_model function in the mlflow/pytorch/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[0.5.0,)

Improper Control of Generation of Code ('Code Injection')

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Improper Control of Generation of Code ('Code Injection') via the _run_entry_point function in the projects/backend/local.py file. An attacker can execute arbitrary code on the victim's system by submitting a maliciously crafted MLproject file.

How to fix Improper Control of Generation of Code ('Code Injection')?

There is no fixed version for mlflow.

[1.11.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_from_pickle function in the mlflow/langchain/utils.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[2.5.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_custom_objects function in the mlflow/tensorflow/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[2.0.0rc0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_model function in the mlflow/lightgbm/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[1.23.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_model function in the pmdarima/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[1.24.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_model_from_local_file function in the sklearn/__init__.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model, which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[1.1.0,)

Deserialization of Untrusted Data

mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the _load_pyfunc function in the mlflow/pyfunc/model.py file. An attacker can execute arbitrary code on the victim's system by injecting a malicious pickle object into a PyFunc model which will then be deserialized when the model is loaded.

How to fix Deserialization of Untrusted Data?

There is no fixed version for mlflow.

[0.9.0,)

mlflow@3.14.0

MLflow is an open source platform for the complete machine learning lifecycle

latest version

first published

latest version published

licenses detected

Direct Vulnerabilities

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