Auth bypass in Cvat Computer Vision Annotation Tool
CVE-2025-54573
CVAT is an open source interactive video and image annotation tool for computer vision. In versions 1.1.0 through 2.41.0, email verification was not enforced when using Basic HTTP Authentication. As a result, users could create accounts using fake email addresses and use the product as verified users. Additionally, the missing email verification check leaves the system open to bot signups and further usage. CVAT 2.42.0 and later versions contain a fix for the issue. CVAT Enterprise customers have a workaround available; those customers may disable registration to prevent this issue.
Vulnerability class: Broken Authentication
EPSS: 0.003 (19.5th percentile) — read the EPSS interpretation.
CVSS v3 metric
CVSS v3 base score 4.3 (Medium). Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L.
Affected products
- Cvat Computer_vision_annotation_tool
- Cvat-ai Cvat — versions >= 1.1.0, < 2.42.0
Weakness classification (CWE)
References
- security-advisories@github.com (x_refsource_CONFIRM, Vendor Advisory)
- security-advisories@github.com (Patch, x_refsource_MISC)
Frequently asked questions
- What is CVE-2025-54573?
- CVE-2025-54573 is a medium-severity vulnerability in Cvat Computer Vision Annotation Tool, classified under Improper Authentication. CVSS score: 4.3/10. Published 2025-07-30.
- How severe is CVE-2025-54573?
- Medium severity. CVSS v3 base score is 4.3 out of 10.