Label Studio is an a popular open source data labeling tool. Versions prior to 1.9.2 have a cross-site scripting (XSS) vulnerability that could be exploited when an authenticated user uploads a crafted image file for their avatar that gets rendered as a HTML file on the website. Executing arbitrary JavaScript could result in an attacker performing malicious actions on Label Studio users if they visit the crafted avatar image. For an example, an attacker can craft a JavaScript payload that adds a new Django Super Administrator user if a Django administrator visits the image. The file `users/functions.py` lines 18-49 show that the only verification check is that the file is an image by extracting the dimensions from the file. Label Studio serves avatar images using Django's built-in `serve` view, which is not secure for production use according to Django's documentation. The issue with the Django `serve` view is that it determines the `Content-Type` of the response by the file extension in the URL path. Therefore, an attacker can upload an image that contains malicious HTML code and name the file with a `.html` extension to be rendered as a HTML page. The only file extension validation is performed on the client-side, which can be easily bypassed. Version 1.9.2 fixes this issue. Other remediation strategies include validating the file extension on the server side, not in client-side code; removing the use of Django's `serve` view and implement a secure controller for viewing uploaded avatar images; saving file content in the database rather than on the filesystem to mitigate against other file related vulnerabilities; and avoiding trusting user controlled inputs.
This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 7.1, indicating it can be exploited remotely over the network with relatively low complexity though user interaction is required and does not require pre-existing privileges . The vulnerability impacts limited data confidentiality, limited integrity, and limited availability for affected systems. Impacting 1 product from humansignal organizations running these solutions should prioritize assessment and patching.
Reported in 2024, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2024-01-23T23:15:08.100
2024-11-21T08:29:48.500
Modified
CVSSv3.1: 7.1 (HIGH)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | humansignal | label_studio | < 1.9.2 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For humansignal's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.