Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:LCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-47115 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Humansignal Label Studio. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked in the top 27% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Label Studio versions prior to 1.9.2 contain a cross-site scripting vulnerability in the avatar upload functionality. The affected code in users/functions.py performs only a client-side image dimension check before storing the file, after which Django's built-in serve view delivers the content using the file extension from the URL to set the Content-Type header. An attacker can therefore supply a file containing HTML and JavaScript that is rendered directly in a victim's browser.
An authenticated user can upload a maliciously named avatar image that bypasses the client-side filter. When another user, particularly a Django administrator, views the avatar, the embedded script executes with the victim's privileges, enabling actions such as creation of a new superuser account.
The GitHub Security Advisory and the 1.9.2 patch recommend upgrading to the fixed release. Additional hardening steps include performing server-side file-extension validation, replacing Django's serve view with a dedicated controller that enforces safe Content-Type handling, and storing avatar content in the database rather than the filesystem.
The EPSS score rose from a low baseline to a peak of 0.27 on 2025-01-22 before receding, indicating a period of increased exploitation interest after public disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0082
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Label Studio is an open-source data labeling platform used in AI/ML workflows for annotating datasets, fitting under 'Other Platforms' as it supports data preparation for various AI tasks like computer vision and NLP.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
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 target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
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.
A.5.6 only promotes membership in groups to improve knowledge, receive alerts, and share information; it performs no action that stops XSS exploitation (or even the underlying neutralization defect) and is a pure dependency for other controls per the owner's labeling rule.