CVE-2023-6014 in mlflow
Summary
by MITRE • 11/16/2023
An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment.
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Analysis
by VulDB Data Team • 12/14/2023
The vulnerability identified as CVE-2023-6014 represents a critical authentication bypass flaw in MLflow, a popular machine learning platform that manages the entire machine learning lifecycle. This vulnerability allows unauthenticated attackers to create arbitrary user accounts within the MLflow system, effectively circumventing all established authentication mechanisms. The flaw exists in the account creation process where proper validation and authorization checks are either missing or improperly implemented, enabling malicious actors to exploit this weakness without requiring legitimate credentials or prior access to the system.
This authentication bypass vulnerability falls under the CWE-287 category of "Improper Authentication" and specifically relates to CWE-305 "Authentication Bypass by Primary Weakness" and CWE-307 "Too Many Failures to Login." The technical implementation flaw likely stems from insufficient input validation during user registration or account creation APIs that do not properly verify the identity of the requester or enforce proper access controls. The vulnerability exists at the application layer where the MLflow service fails to validate whether the account creation request originates from an authenticated user or if it should be permitted based on established security policies.
The operational impact of this vulnerability is severe and multifaceted, particularly in enterprise environments where MLflow is used to manage sensitive machine learning models, data, and experiments. An attacker who successfully exploits this vulnerability can establish persistent access to the MLflow platform, potentially gaining access to proprietary algorithms, training datasets, and model artifacts that may contain intellectual property or sensitive information. The ability to create accounts without authentication also enables attackers to manipulate the system by creating multiple user accounts to perform unauthorized activities such as modifying experiments, accessing restricted resources, or even conducting privilege escalation attacks. This vulnerability directly impacts the confidentiality, integrity, and availability of the MLflow environment, as it allows unauthorized access to the entire platform's functionality.
Organizations using MLflow should immediately implement mitigations to address this vulnerability, starting with applying the latest security patches provided by the MLflow maintainers. Network-level mitigations should include restricting access to MLflow services through firewalls and implementing strict access controls that limit which IP addresses can reach the MLflow platform. The platform should be configured to require authentication for all account creation and management functions, with proper rate limiting to prevent abuse of the registration functionality. Additionally, organizations should implement robust monitoring and logging of account creation activities to detect suspicious behavior and ensure that only authorized personnel can create new user accounts. The mitigation strategy should align with the ATT&CK framework's T1078.004 technique for Valid Accounts and T1566.001 for Phishing, as this vulnerability enables attackers to establish persistent access and potentially expand their foothold within the environment. Organizations should also conduct thorough security assessments to identify any other potential authentication bypass vulnerabilities in their MLflow deployments and ensure proper network segmentation to limit the impact of any successful exploitation attempts.