CVE-2024-2213 is a security vulnerability identified in the zenml-io/zenml open-source machine learning operations (MLOps) framework, specifically in versions up to and including 0.55.4. The vulnerability is classified under CWE-620, which relates to unverified password changes. The root cause is an improper authentication mechanism that allows an attacker who already has access to an active user session to change the account password without providing the current password. This bypasses the standard security control that ensures only the legitimate user can change their password by verifying knowledge of the existing password. The vulnerability does not require additional user interaction but does require the attacker to have some level of authenticated access (low privileges) to the session. Exploitation could lead to unauthorized account takeover, allowing the attacker to maintain persistent access or escalate privileges within the affected system. The CVSS v3.0 base score is 3.3 (low severity), reflecting that the attack vector is local (AV:L), requires low attack complexity (AC:L), low privileges (PR:L), no user interaction (UI:N), and impacts only integrity (I:L) without affecting confidentiality or availability. No known exploits are reported in the wild as of the publication date. The vulnerability was fixed in version 0.56.3 by improving authentication checks during the password change process. Organizations using zenml-io/zenml should upgrade to the patched version to prevent potential account takeovers.

For European organizations, the impact of CVE-2024-2213 is primarily related to account integrity within the zenml-io/zenml platform. Unauthorized password changes can lead to account takeover, which may allow attackers to manipulate machine learning workflows, access sensitive project data, or disrupt operations. While confidentiality and availability are not directly affected, compromised accounts could be leveraged for further attacks or data manipulation, potentially undermining trust in ML pipelines. The requirement for an active session limits remote exploitation but raises concerns in environments where session hijacking or insider threats are possible. Organizations heavily reliant on zenml-io/zenml for MLOps may face operational risks and reputational damage if attackers exploit this vulnerability. The low CVSS score indicates limited severity, but the risk increases if attackers can gain session access through other means. European entities in sectors such as finance, healthcare, and manufacturing, which increasingly use ML workflows, should be vigilant. Failure to patch could result in unauthorized access to critical ML infrastructure and data integrity issues.

To mitigate CVE-2024-2213, European organizations should: 1) Immediately upgrade zenml-io/zenml installations to version 0.56.3 or later, where the vulnerability is fixed. 2) Implement strict session management controls, including session timeouts and detection of anomalous session activity, to reduce the risk of session hijacking. 3) Enforce multi-factor authentication (MFA) to strengthen user authentication beyond session tokens. 4) Monitor account activity logs for unusual password change attempts or session anomalies. 5) Educate users about the risks of session sharing and the importance of logging out from shared or public devices. 6) Consider network segmentation and access controls to limit exposure of zenml-io/zenml services to trusted users only. 7) Regularly audit and review user privileges to minimize the number of users with active sessions that could be exploited. 8) Integrate vulnerability scanning and patch management processes to ensure timely updates of open-source components like zenml-io/zenml.