CVE-2024-25723 Scanner
Detects 'Improper Authentication' vulnerability in ZenML affects before v. 0.46.7.
Short Info
Level
Critical
Single Scan
Single Scan
Can be used by
Asset Owner
Estimated Time
10 seconds
Time Interval
17 days 6 hours
Scan only one
URL
Toolbox
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ZenML is an open-source machine learning development framework that is designed to simplify the process of setting up and maintaining a machine learning pipeline. Used by data scientists, software developers, and researchers, ZenML standardizes and automates common tasks within the machine learning workflow. It is implemented in Python and offers compatibility with popular ML tools and libraries, facilitating seamless integration into existing systems. As companies and individuals employ ZenML to streamline their machine learning processes, ensuring a secure environment is essential to protecting sensitive data. Therefore, monitoring and addressing vulnerabilities in ZenML is critical for maintaining the integrity and reliability of machine learning operations.
The Improper Authentication vulnerability in ZenML Server stems from insufficient validation in the /api/v1/users/{user_name_or_id}/activate endpoint. Specifically, it allows privilege escalation by accepting authentication based on a valid username without additional security checks. This flaw is particularly concerning as it can be exploited remotely, potentially leading to unauthorized system control and data exposure. If exploited, attackers could bypass legitimate authentication processes, gaining unauthorized access to the application. This vulnerability emphasizes the need for rigorous access control and validation in deployment endpoints. As improper authentication mechanisms are frequently targeted by attackers, resolving such issues is paramount to preventing unauthorized access.
Technically, the vulnerability is focused on the authentication mechanism used by ZenML Server's REST API endpoint /api/v1/users/{user_name_or_id}/activate. Exploiting this vulnerability requires crafting a request that can trick the server into authorizing a user based on a valid username and a new password specified in the request body. Attackers might manipulate the request headers or payloads to achieve unauthorized access, highlighting weaknesses in the access control logic. These conditions illustrate how relatively simple flaws in endpoint logic can lead to significant security issues. Properly securing this endpoint is essential to maintain the confidentiality and integrity of user accounts and data. Additionally, thorough testing of all API endpoints should be conducted to prevent similar flaws.
When exploited, this vulnerability may have substantial consequences including unauthorized access to sensitive data, user account takeover, and potential data manipulation or deletion. Attackers could use the access gained to alter configurations, extract confidential information, or execute unauthorized commands. The risk of privilege escalation means that sensitive administrative functions or data could become compromised. Organizations could face legal liabilities and reputational damage in the wake of successful exploitation. Ensuring robust authentication logic and comprehensive access controls is essential to prevent these potential adverse outcomes and maintain system security.
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