CVE-2022-31188 is a Server-Side Request Forgery (SSRF) vulnerability identified in versions of the open-source computer vision annotation tool CVAT prior to 2.0.0. CVAT is widely used for interactive video and image annotation, facilitating machine learning and computer vision workflows. The vulnerability arises from insufficient validation of URLs in the affected code path, allowing an attacker to craft malicious requests that the server then executes. SSRF vulnerabilities enable attackers to induce the server to make HTTP requests to arbitrary internal or external resources, potentially bypassing network access controls. This can lead to unauthorized access to internal systems, exposure of sensitive data, or interaction with internal services that are not directly accessible from the internet. The vulnerability was addressed in CVAT version 2.0.0 by adding validation mechanisms for URLs to prevent malicious request redirection. No known exploits have been reported in the wild, and no workarounds exist aside from upgrading to the patched version. The vulnerability is categorized under CWE-918, which covers SSRF issues where the server is tricked into sending requests to unintended locations. Given the nature of CVAT as a web-based annotation tool, the SSRF could be triggered by an attacker with access to the application interface, potentially leading to internal network reconnaissance or exploitation of other internal services. The lack of authentication requirements is not explicitly stated, but typically CVAT deployments are within organizational environments with some access controls, which may limit exposure. However, if exposed to untrusted users, the risk increases significantly.

For European organizations using CVAT versions prior to 2.0.0, this SSRF vulnerability poses a risk of internal network reconnaissance and potential exploitation of internal services that are otherwise inaccessible externally. This could lead to unauthorized disclosure of sensitive information, compromise of internal systems, or pivoting attacks within the network. Organizations in sectors such as automotive, manufacturing, research institutions, and technology companies that rely on CVAT for computer vision annotation are particularly at risk. The impact on confidentiality is moderate to high if internal services contain sensitive data. Integrity and availability impacts are possible if the SSRF is leveraged to interact with internal APIs or services that can be manipulated or disrupted. Since CVAT is often deployed in internal or cloud environments, the SSRF could be used to access metadata services or internal management interfaces, increasing the attack surface. The absence of known exploits reduces immediate risk, but the vulnerability remains a concern until patched. European organizations with exposed or poorly segmented CVAT instances face increased risk of lateral movement and data exposure.

The primary and most effective mitigation is to upgrade CVAT installations to version 2.0.0 or later, where URL validation has been implemented to prevent SSRF attacks. Organizations should audit their CVAT deployments to identify versions in use and prioritize patching. Additionally, network segmentation should be enforced to limit the CVAT server's ability to reach sensitive internal services. Implement strict egress filtering and firewall rules to restrict outbound HTTP requests from the CVAT server to only necessary destinations. Employ web application firewalls (WAFs) with SSRF detection capabilities to monitor and block suspicious request patterns. Review and enforce strong access controls on CVAT interfaces to prevent unauthorized users from exploiting the vulnerability. Logging and monitoring should be enhanced to detect unusual outbound requests from the CVAT server. If upgrading immediately is not feasible, consider isolating the CVAT server within a restricted network zone and disabling any unnecessary network access to reduce the attack surface. Regularly review CVAT configuration and network policies to ensure adherence to the principle of least privilege.