Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:L/A:NSummary
CVE-2024-37306 is a high-severity CSRF (CWE-352) vulnerability in Cvat Computer Vision Annotation Tool. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 11th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SC-23 (Session Authenticity) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-36567
Vulnerability Data
Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. Starting in version 2.2.0 and prior to version 2.14.3, if an attacker can trick a logged-in CVAT user into visiting a malicious URL, they…
more
can initiate a dataset export or a backup from a project, task or job that the victim user has permission to export into a cloud storage that the victim user has access to. The name of the resulting file can be chosen by the attacker. This implies that the attacker can overwrite arbitrary files in any cloud storage that the victim can access and, if the attacker has read access to the cloud storage used in the attack, they can obtain media files, annotations, settings and other information from any projects, tasks or jobs that the victim has permission to export. Version 2.14.3 contains a fix for the issue. No known workarounds are available.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V3.3.2V3.5.1V10.2.1
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
Protecting session authenticity prevents attackers from replaying or forging authenticated requests via the victim's browser.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require anti-CSRF controls such as tokens or SameSite attributes.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.
Contextual intelligence about emerging CSRF toolkits can be translated into updated anti-CSRF token or same-site policy configurations across applications.