Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2025-25296 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Humansignal Label Studio. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 23% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
CVE-2025-25296 is a cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting Label Studio, an open source data labeling tool, in versions prior to 1.16.0. The flaw exists in the `/projects/upload-example` endpoint, which permits injection of arbitrary HTML via a GET request using a specially crafted `label_config` query parameter. Attackers can supply a maliciously formatted XML label config with inline task data containing HTML/JavaScript, which the endpoint renders without proper sanitization.
The vulnerability enables exploitation over the network with low complexity and no privileges required, though it depends on user interaction, earning a CVSS v3.1 base score of 6.1 (AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N). An attacker crafts a malicious URL targeting the endpoint and tricks victims into visiting it, leading to arbitrary JavaScript execution in their browsers within the Label Studio context. Although a Content Security Policy is present, its report-only mode renders it ineffective against script execution. Successful attacks can result in theft of sensitive data, session hijacking, or other client-side malicious actions.
Label Studio version 1.16.0 includes a patch addressing the issue. For mitigation details, refer to the GitHub security advisory at https://github.com/HumanSignal/label-studio/security/advisories/GHSA-wpq5-3366-mqw4 and the patching commit at https://github.com/HumanSignal/label-studio/commit/8cf6958e1e27ef6a03ed287e674470975d340885.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-4106
Vulnerability Data
Label Studio is an open source data labeling tool. Prior to version 1.16.0, Label Studio's `/projects/upload-example` endpoint allows injection of arbitrary HTML through a `GET` request with an appropriately crafted `label_config` query parameter. By crafting a specially formatted XML label…
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config with inline task data containing malicious HTML/JavaScript, an attacker can achieve Cross-Site Scripting (XSS). While the application has a Content Security Policy (CSP), it is only set in report-only mode, making it ineffective at preventing script execution. The vulnerability exists because the upload-example endpoint renders user-provided HTML content without proper sanitization on a GET request. This allows attackers to inject and execute arbitrary JavaScript in victims' browsers by getting them to visit a maliciously crafted URL. This is considered vulnerable because it enables attackers to execute JavaScript in victims' contexts, potentially allowing theft of sensitive data, session hijacking, or other malicious actions. Version 1.16.0 contains a patch for the issue.
- CWE(s)
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.