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mlflow@2.19.0 vulnerabilities

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

  • latest version

    2.19.0

  • first published

    6 years ago

  • latest version published

    10 days ago

  • licenses detected

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    Direct Vulnerabilities

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

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    VulnerabilityVulnerable Version
    • 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,)
    • H
    Path Traversal

    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 Path Traversal due to improper sanitization of user-supplied paths in the artifact deletion functionality. An attacker can delete arbitrary directories on the server's filesystem by exploiting the double decoding process in the _delete_artifact_mlflow_artifacts handler and local_file_uri_to_path function. This vulnerability arises from an additional unquote operation in the delete_artifacts function of local_artifact_repo.py, which fails to adequately prevent path traversal sequences.

    How to fix Path Traversal?

    There is no fixed version for mlflow.

    [0,)
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