CVE-2023-41882 in vantage6
Summary
by MITRE • 10/25/2023
vantage6 is privacy preserving federated learning infrastructure. The endpoint /api/collaboration/{id}/task is used to collect all tasks from a certain collaboration. To get such tasks, a user should have permission to view the collaboration and to view the tasks in it. However, prior to version 4.0.0, it is only checked if the user has permission to view the collaboration. Version 4.0.0 contains a patch. There are no known workarounds.
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Analysis
by VulDB Data Team • 10/31/2023
The vantage6 platform represents a privacy-preserving federated learning infrastructure designed to enable collaborative machine learning without exposing sensitive data. This system facilitates secure computation across multiple organizations by maintaining data locality while allowing model training across distributed datasets. The platform's architecture relies on role-based access control mechanisms to protect sensitive collaboration and task information, ensuring that only authorized users can access specific federated learning operations. The vulnerability under discussion affects the platform's permission validation logic within its RESTful API endpoints.
The technical flaw exists in the /api/collaboration/{id}/task endpoint which serves to collect all tasks associated with a specific collaboration. Prior to version 4.0.0, the access control implementation contained a critical logic error where the system only verified whether a user possessed permission to view the collaboration itself, but failed to validate whether the user had adequate permissions to view the individual tasks within that collaboration. This represents a classic privilege escalation vulnerability where insufficient authorization checks allow unauthorized access to sensitive data. The vulnerability stems from a missing authorization layer that should have enforced task-level permissions in addition to collaboration-level permissions, creating a security boundary violation.
The operational impact of this vulnerability is significant as it enables unauthorized users to access task information from collaborations they should not be permitted to view. This could potentially expose sensitive model training details, including task parameters, execution metadata, and other confidential information about federated learning operations. Attackers could exploit this weakness to gain insights into ongoing research projects, understand the nature of machine learning models being developed, and potentially identify vulnerabilities in the training processes. The implications extend beyond simple data exposure to include potential intellectual property theft and competitive intelligence gathering, particularly in industries where proprietary machine learning models represent significant business value.
The vulnerability aligns with CWE-285, which addresses improper authorization in software systems, and demonstrates a clear failure in the principle of least privilege enforcement. From an ATT&CK framework perspective, this represents a privilege escalation technique that could be leveraged to move laterally within the system and access additional resources. Organizations using vantage6 versions prior to 4.0.0 should immediately implement the available patch and conduct comprehensive access control audits. The lack of known workarounds means that organizations must upgrade to version 4.0.0 or later to remediate this vulnerability. Security teams should also review existing access control policies and implement additional monitoring to detect unauthorized access attempts to collaboration and task endpoints. The fix in version 4.0.0 should include comprehensive logging of access attempts and proper authorization validation at both collaboration and task levels to prevent similar issues in future releases.