Detects 'Path Traversal' vulnerability in mlflow/mlflow affects v. before 2.5.0.
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CVE-2023-3765 Scanner Detail
mlflow/mlflow is a software developed for the purpose of managing machine learning workflows. It is an open-source platform that enables data scientists and engineers to track experiments, package code and models and manage them in a reproducible manner. With its powerful tools and user-friendly interface, it has become a popular choice for many organizations working with machine learning models.
CVE-2023-3765 is a critical vulnerability detected in mlflow/mlflow prior to version 2.5.0. This vulnerability allows a malicious actor to perform an absolute path traversal attack. This can be achieved by manipulating the URL and accessing arbitrary files on the server. An attacker can use this exploit to steal sensitive information, modify files or even disrupt the entire system.
Exploiting this vulnerability can lead to disastrous consequences. In the worst-case scenario, an attacker could gain complete control of the system and access sensitive data. They could also cause significant damage by deleting important files or modifying data, potentially causing a massive financial loss to the organization. Overall, this exploit poses a severe threat to the security and functionality of the system.
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