Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2026-25733 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Cern Rucio. Its CVSS base score is 7.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 18th percentile by exploit likelihood (below the median); 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 SC-23 (Session Authenticity) — 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-2026-25733 is a stored Cross-Site Scripting (XSS) vulnerability in Rucio, a software framework for organizing, managing, and accessing large volumes of scientific data using customizable policies. The flaw affects the Custom Rules function of the WebUI in versions prior to 35.8.3, 38.5.4, and 39.3.1, where attacker-controlled input is persisted by the backend and later rendered without proper output encoding. This enables arbitrary JavaScript execution in the context of the WebUI for users viewing affected pages. The vulnerability carries a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N) and maps to CWE-79 (Improper Neutralization of Input During Web Page Generation) and CWE-1004 (Sensitive Cookie Without 'HttpOnly' Flag, though primarily driven by XSS).
An attacker with low privileges (PR:L), such as an authenticated user able to access the Custom Rules function, can submit malicious input that gets stored. Subsequent users who view pages displaying this input in the WebUI trigger JavaScript execution in their browser context, given the requirement for user interaction (UI:R). Successful exploitation can lead to high-impact confidentiality and integrity violations, including session token theft or performing unauthorized actions on behalf of victims, all over the network with low attack complexity.
Mitigation is provided by upgrading to Rucio versions 35.8.3, 38.5.4, or 39.3.1, which address the output encoding issue, as documented in the GitHub release notes for these versions and the security advisory GHSA-rwj9-7j48-9f7q. Additional guidance on XSS prevention, including output encoding best practices, is available in the OWASP Cross-Site Scripting Prevention Cheat Sheet.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8726
Vulnerability Data
Rucio is a software framework that provides functionality to organize, manage, and access large volumes of scientific data using customizable policies. Versions prior to 35.8.3, 38.5.4, and 39.3.1 have a stored Cross-Site Scripting (XSS) vulnerability in the Custom Rules function…
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of the WebUI where attacker-controlled input is persisted by the backend and later rendered in the WebUI without proper output encoding. This allows arbitrary JavaScript execution in the context of the WebUI for users who view affected pages, potentially enabling session token theft or unauthorized actions. Versions 35.8.3, 38.5.4, and 39.3.1 fix 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.
Proper implementation of session authenticity requires marking sensitive session cookies HttpOnly so that client scripts cannot access them.
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).
Role-based training can reduce the chance developers introduce missing HttpOnly flags but supplies no enforcement or detection, leaving essentially all of the implementation flaw's risk intact.
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.
Application security requirements can mandate HttpOnly on sensitive cookies.
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.