Vulnerabilities

CVAT before 2.69.0 contains an improper authorization vulnerability in QualityReportViewSet.get_queryset that allows authenticated attackers to enumerate quality report identifiers belonging to other organizations by exploiting a missing check_object_permissions call on the parent_id query parameter of the quality reports API endpoint. Attackers can send requests with sequential integer parent_id values and distinguish between existing and non-existing reports via HTTP 500 versus HTTP 404 response differences, disclosing cross-organization report existence without returning report content.

CVAT is an open source interactive video and image annotation tool for computer vision. In versions 2.2.0 through 2.54.0, an attacker is able to execute arbitrary JavaScript in a victim user's CVAT UI session, provided that they are able to create a maliciously crafted label in a CVAT task or project, then get the victim user to either edit that label, or view a shape that refers to that label; and/or get the victim user to upload a maliciously crafted SVG image when configuring a skeleton. This gives the attacker temporary access to all CVAT resources that the victim user can access. Version 2.55.0 fixes the issue.

CVAT is an open source interactive video and image annotation tool for computer vision. In versions 2.8.1 through 2.52.0, an attacker with an account on a CVAT instance is able to retrieve the contents of any file system directory accessible to the CVAT server. The exposed information is names of contained files and subdirectories. The contents of files are not accessible. Version 2.53.0 contains a patch. No known workarounds are available.

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.

CVAT is an open source interactive video and image annotation tool for computer vision. Versions 2.2.0 through 2.39.0 have no validation during the import process of a project or task backup to check that the filename specified in the query parameter refers to a TUS-uploaded file belonging to the same user. As a result, if an attacker with a CVAT account and a `user` role knows the filenames of other users' uploads, they could potentially access and steal data by creating projects or tasks using those files. This issue does not affect annotation or dataset TUS uploads, since in this case object-specific temporary directories are used. Users should upgrade to CVAT 2.40.0 or a later version to receive a patch. No known workarounds are available.

Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. In versions starting from 2.4.0 to before 2.38.0, an authenticated CVAT user may be able to retrieve the IDs and names of all tasks, projects, labels, and the IDs of all jobs and quality reports on the CVAT instance. In addition, if the instance contains many resources of a particular type, retrieving this information may tie up system resources, denying access to legitimate users. This issue has been patched in version 2.38.0.

Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. An attacker with an account on an affected CVAT instance is able to run arbitrary code in the context of the Nuclio function container. This vulnerability affects CVAT deployments that run any of the serverless functions of type tracker from the CVAT Git repository, namely TransT and SiamMask. Deployments with custom functions of type tracker may also be affected, depending on how they handle state serialization. If a function uses an unsafe serialization library such as pickle or jsonpickle, it's likely to be vulnerable. Upgrade to CVAT 2.26.0 or later. If you are unable to upgrade, shut down any instances of the TransT or SiamMask functions you're running.

Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. If an attacker can trick a logged-in CVAT user into visiting a maliciously-constructed URL, they can initiate any API calls on that user's behalf. This gives the attacker temporary access to all data that the victim user has access to. Upgrade to CVAT 2.19.0 or a later version to fix this issue.

Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. An attacker with a CVAT account can access webhook delivery information for any webhook registered on the CVAT instance, including that of other users. For each delivery, this contains information about the event that caused the delivery, typically including full details about the object on which an action was performed (such as the task for an "update:task" event), and the user who performed the action. In addition, the attacker can redeliver any past delivery of any webhook, and trigger a ping event for any webhook. Upgrade to CVAT 2.18.0 or any later version.

Computer Vision Annotation Tool (CVAT) is an interactive video and image annotation tool for computer vision. CVAT allows users to supply custom endpoint URLs for cloud storages based on Amazon S3 and Azure Blob Storage. Starting in version 2.1.0 and prior to version 2.14.3, an attacker with a CVAT account can exploit this feature by specifying URLs whose host part is an intranet IP address or an internal domain name. By doing this, the attacker may be able to probe the network that the CVAT backend runs in for HTTP(S) servers. In addition, if there is a web server on this network that is sufficiently API-compatible with an Amazon S3 or Azure Blob Storage endpoint, and either allows anonymous access, or allows authentication with credentials that are known by the attacker, then the attacker may be able to create a cloud storage linked to this server. They may then be able to list files on the server; extract files from the server, if these files are of a type that CVAT supports reading from cloud storage (media data (such as images/videos/archives), importable annotations or datasets, task/project backups); and/or overwrite files on this server with exported annotations/datasets/backups. The exact capabilities of the attacker will depend on how the internal server is configured. Users should upgrade to CVAT 2.14.3 to receive a patch. In this release, the existing SSRF mitigation measures are applied to requests to cloud providers, with access to intranet IP addresses prohibited by default. Some workarounds are also available. One may use network security solutions such as virtual networks or firewalls to prohibit network access from the CVAT backend to unrelated servers on your internal network and/or require authentication for access to internal servers.

CVAT is an opensource interactive video and image annotation tool for computer vision. Versions prior to 2.0.0 were found to be subject to a Server-side request forgery (SSRF) vulnerability. Validation has been added to urls used in the affected code path in version 2.0.0. Users are advised to upgrade. There are no known workarounds for this issue.

CVE-2021-45046 is a remote code execution and information disclosure flaw in Apache Log4j2 (CWE-917) that resulted from an incomplete fix to CVE-2021-44228 (Log4Shell), leaving the Thread Context Lookup Pattern vulnerable in certain non-default configurations. It is triggered when an application logs attacker-controlled data using layouts or patterns that perform Thread Context (MDC) lookups, allowing crafted lookup expressions to be evaluated against untrusted input. A successful attacker can achieve remote code execution, or potentially information disclosure, on the affected service. Any deployment of Apache Log4j2 that relies on the affected non-default lookup configurations is exposed, which given Log4j2's ubiquity in Java applications and embedded products means a very large installed base. Exploitation is confirmed: the flaw is in CISA KEV (added 2023-05-01) with known ransomware use, and EPSS assigns a 100% probability of exploitation within 30 days.

Do: Upgrade Log4j2 to 2.17.0 or later per vendor instructions (or 2.12.3/2.3.1 for the legacy 2.12/2.3 branches), since the 2.16.0 fix was itself incomplete in some non-default configurations. Where upgrading is not immediately possible, remove the JndiLookup class from the Log4j2 jar or disable lookup processing, and audit applications and dependencies that bundle Log4j2 while following the CISA KEV required action to apply vendor updates.