Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:L/A:NSummary
CVE-2026-21873 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Zauberzeug Nicegui. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 15th 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 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-2026-21873 is a vulnerability in NiceGUI, a Python-based UI framework, affecting versions 2.22.0 through 3.4.1. The issue arises from an unsafe implementation in the pushstate event listener used by the ui.sub_pages feature, which permits manipulation of the URL fragment identifier even from a cross-site context via an iframe. Published on 2026-01-08, it has a CVSS v3.1 base score of 7.2 (AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:L/A:N) and is classified under CWE-79.
An unauthenticated attacker with network access can exploit this vulnerability without requiring user interaction, as indicated by the low attack complexity and changed scope in the CVSS vector. By embedding the affected NiceGUI application in an iframe from a malicious site, the attacker can manipulate the URL fragment identifier cross-site, potentially leading to limited impacts on confidentiality and integrity, such as unauthorized access to or alteration of client-side state.
The vulnerability has been patched in NiceGUI version 3.5.0. Security advisories recommend updating to this version or later to mitigate the issue. Additional details are provided in the release notes at https://github.com/zauberzeug/nicegui/releases/tag/v3.5.0 and the GitHub security advisory at https://github.com/zauberzeug/nicegui/security/advisories/GHSA-mhpg-c27v-6mxr.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1475
Vulnerability Data
NiceGUI is a Python-based UI framework. From versions 2.22.0 to 3.4.1, an unsafe implementation in the pushstate event listener used by ui.sub_pages allows an attacker to manipulate the fragment identifier of the URL, which they can do despite being cross-site,…
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using an iframe. This issue has been patched in version 3.5.0.
- 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.