Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2026-44581 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Vercel Next.Js. Its CVSS base score is 4.7 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 13th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SI-15 (Information Output Filtering) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-30083
Vulnerability Data
Next.js is a React framework for building full-stack web applications. From 13.4.0 to before 15.5.16 and 16.2.5, App Router applications that rely on CSP nonces can be vulnerable to stored cross-site scripting when deployed behind shared caches. In affected versions,…
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malformed nonce values derived from request headers could be reflected into rendered HTML in an unsafe way, allowing an attacker to poison cached responses and cause script execution for later visitors. This vulnerability is fixed in 15.5.16 and 16.2.5.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS via cache poisoning in public-facing Next.js web app directly enables exploitation of the application for initial access and client-side script execution.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly validates request-header-derived nonce values before they are incorporated into rendered HTML, blocking the malformed input that enables unsafe reflection and cache poisoning.
Filters or sanitizes dynamic output (including nonce attributes) so that attacker-controlled values cannot produce executable script in cached responses.
Protects information in shared resources such as reverse-proxy caches, limiting the ability of a poisoned response containing a malicious nonce to affect subsequent visitors.
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.