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CVE-2023-6018 Scanner

Detects 'Improper Access Control' vulnerability in mlflow/mlflow affects v. Unknown.

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Short Info


Level

Critical

Type

Single Scan

Can be used by

Asset Owner

Estimated Time

10 sec

Scan only one

Domain, Ipv4

Parent Category

CVE-2023-6018 Scanner Detail

MLflow is an open-source platform for managing and tracking machine learning experiments. It allows data scientists to easily track experiments, package and share their models, and collaborate with their colleagues. The platform is widely used in data science communities, and it has become an essential tool for machine learning teams looking to manage data, models, and experiments.

The CVE-2023-6018 vulnerability was detected in MLflow, which allows an attacker to overwrite files on the server that is hosting the platform without any authentication. This means that an attacker can exploit this vulnerability remotely to access user data and take complete control of the server. It opens up a backdoor to access highly sensitive information and steal intellectual property.

The exploitation of the vulnerability can lead to enormous damage to the server and machine learning models, resulting in unauthorized access to private and confidential information. Attackers can steal sensitive data or inject malicious traffic to harm the user. The vulnerability can result in compromising user privacy and the security of confidential data. It can also potentially lead to unauthorized modifications of machine learning models, causing a loss of credibility in the models and the products they are used in.

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