Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:N/A:NSummary
CVE-2024-23633 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Humansignal Label Studio. Its CVSS base score is 4.7 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0084
Vulnerability Data
Label Studio, an open source data labeling tool had a remote import feature allowed users to import data from a remote web source, that was downloaded and could be viewed on the website. Prior to version 1.10.1, this feature could…
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had been abused to download a HTML file that executed malicious JavaScript code in the context of the Label Studio 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. `data_import/uploader.py` lines 125C5 through 146 showed that if a URL passed the server side request forgery verification checks, the contents of the file would be downloaded using the filename in the URL. The downloaded file path could then be retrieved by sending a request to `/api/projects/{project_id}/file-uploads?ids=[{download_id}]` where `{project_id}` was the ID of the project and `{download_id}` was the ID of the downloaded file. Once the downloaded file path was retrieved by the previous API endpoint, `data_import/api.py`lines 595C1 through 616C62 demonstrated that the `Content-Type` of the response was determined by the file extension, since `mimetypes.guess_type` guesses the `Content-Type` based on the file extension. Since the `Content-Type` was determined by the file extension of the downloaded file, an attacker could import in a `.html` file that would execute JavaScript when visited. Version 1.10.1 contains a patch for this issue. Other remediation strategies are also available. For all user provided files that are downloaded by Label Studio, set the `Content-Security-Policy: sandbox;` response header when viewed on the site. The `sandbox` directive restricts a page's actions to prevent popups, execution of plugins and scripts and enforces a `same-origin` policy. Alternatively, restrict the allowed file extensions that may be downloaded.
- 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 AI data preparation.
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.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.