CVE-2024-47172 in cvat
Summary
by MITRE • 09/30/2024
Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. An attacker with a CVAT account may retrieve certain information about any project, task, job or membership resource on the CVAT instance. The information exposed in this way is the same as the information returned on a GET request to the resource. In addition, the attacker can also alter the default source and target storage associated with any project or task. Upgrade to CVAT 2.19.1 or any later version to fix the issue.
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Analysis
by VulDB Data Team • 10/31/2024
The Computer Vision Annotation Tool CVAT vulnerability CVE-2024-47172 represents a critical authorization flaw that undermines the security boundaries of the platform. This vulnerability affects the core access control mechanisms within CVAT, an essential tool for computer vision research and development that handles sensitive annotated data from various domains including healthcare, autonomous vehicles, and security systems. The flaw manifests as an insufficient authorization check that allows authenticated users to bypass normal access restrictions and obtain information about resources they should not be able to access.
The technical implementation of this vulnerability stems from inadequate validation of user permissions when processing GET requests to project, task, job, and membership resources. Attackers with valid accounts can exploit this weakness to retrieve sensitive metadata and operational details about any resource within the CVAT instance regardless of their actual role or authorization level. This information disclosure vulnerability aligns with CWE-284 Access Control Issues, specifically targeting insufficient authorization controls that permit unauthorized access to protected resources. The vulnerability operates at the application layer, exploiting the lack of proper access validation logic that should verify user permissions before returning resource information.
The operational impact of this vulnerability extends beyond simple information disclosure, as it also enables attackers to modify critical storage configurations. Specifically, unauthorized users can alter the default source and target storage associated with projects or tasks, potentially leading to data exfiltration, corruption, or manipulation of annotation workflows. This dual nature of the vulnerability creates a significant risk for organizations relying on CVAT for sensitive data processing, as attackers could not only discover project details but also redirect data flows to malicious endpoints. The compromise of storage configurations directly impacts data integrity and confidentiality, creating potential pathways for data loss or unauthorized access to downstream systems.
Security professionals should note that this vulnerability exists in CVAT versions prior to 2.19.1 and represents a regression in access control implementation that violates fundamental security principles of least privilege and principle of least authority. The ATT&CK framework categorizes this issue under T1078 Valid Accounts and T1566 Impersonation, as it leverages legitimate user credentials to access unauthorized resources. Organizations using CVAT should immediately implement the recommended upgrade to version 2.19.1 or later, which includes proper authorization checks and access control validation. Additionally, network segmentation, monitoring of API access patterns, and regular security audits should be implemented to detect potential exploitation attempts. The vulnerability demonstrates the importance of thorough access control testing in collaborative platforms where multiple users with varying permission levels interact with sensitive data assets.
The broader implications of this vulnerability highlight the critical need for robust access control mechanisms in data annotation platforms that handle sensitive information. Given that CVAT is widely used in research institutions, government agencies, and commercial enterprises, the potential for exploitation could result in significant data breaches affecting proprietary research, personal privacy, or national security interests. Organizations should also consider implementing additional security controls such as API rate limiting, detailed access logging, and automated monitoring for unauthorized resource access attempts. The vulnerability serves as a reminder that even seemingly benign access control flaws can create substantial security risks when combined with other exploitation vectors, emphasizing the importance of comprehensive security testing and validation of authorization mechanisms in collaborative software environments.