CVE-2024-2213 is a security vulnerability identified in the zenml-io/zenml open-source machine learning orchestration framework, affecting all versions up to and including 0.55.4. The root cause is an improper authentication mechanism during the password change functionality, categorized under CWE-620 (Unverified Password Change). Specifically, an attacker who has access to an active user session can change the account password without providing the current password, effectively bypassing the standard verification process. This flaw allows an attacker to take over user accounts without needing to know or guess the existing password, provided they can hijack or otherwise gain access to a valid session. The vulnerability does not require user interaction but does require the attacker to have at least limited privileges (an active session). The CVSS v3.0 base score is 3.3 (low severity), with the vector indicating local attack vector (AV:L), low attack complexity (AC:L), low privileges required (PR:L), no user interaction (UI:N), unchanged scope (S:U), no confidentiality impact (C:N), low integrity impact (I:L), and no availability impact (A:N). The issue was resolved in zenml-io/zenml version 0.56.3 by enforcing proper authentication checks during password changes. No public exploits or widespread attacks have been reported to date. This vulnerability primarily threatens the integrity of user accounts by enabling unauthorized password changes, which could lead to account takeover and subsequent misuse of the platform's capabilities.

For European organizations using zenml-io/zenml in their machine learning workflows, this vulnerability poses a risk of unauthorized account takeover if an attacker can gain access to an active user session. Such an account compromise could allow attackers to manipulate machine learning pipelines, access sensitive data processed within the platform, or disrupt operations. While the confidentiality and availability impacts are minimal, the integrity of user accounts and potentially the integrity of ML workflows could be compromised. Organizations in sectors with high reliance on ML orchestration, such as finance, healthcare, and manufacturing, may face operational risks and compliance concerns if unauthorized changes occur. The low CVSS score and requirement for an active session limit the threat scope, but insider threats or session hijacking attacks could exploit this vulnerability. Prompt patching is essential to prevent potential misuse.

European organizations should immediately upgrade zenml-io/zenml installations to version 0.56.3 or later, where the vulnerability has been fixed. Until upgrading is possible, organizations should enforce strict session management policies, including short session timeouts and monitoring for unusual session activity to reduce the risk of session hijacking. Implement multi-factor authentication (MFA) to reduce the likelihood of unauthorized session access. Additionally, audit user activity logs for suspicious password change attempts and restrict access to zenml platforms to trusted networks and users. Employ network segmentation to isolate ML orchestration environments and use endpoint security solutions to detect session hijacking attempts. Finally, educate users about the risks of session theft and encourage secure practices such as logging out after use and avoiding shared workstations.