Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2026-20111 is a medium-severity Use of Hard-coded Credentials (CWE-798) vulnerability in Cisco Prime Infrastructure. Its CVSS base score is 4.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 7th 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 IA-5 (Authenticator Management) and SA-11 (Developer Testing and Evaluation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5423
Vulnerability Data
A vulnerability in the web-based management interface of Cisco Prime Infrastructure could allow an authenticated, remote attacker to conduct a stored cross-site scripting (XSS) attack against users of the interface of an affected system. This vulnerability exists because the web-based…
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management interface does not properly validate user-supplied input. An attacker could exploit this vulnerability by inserting malicious code into specific data fields in the interface. A successful exploit could allow the attacker to execute arbitrary script code in the context of the affected interface or access sensitive, browser-based information. To exploit this vulnerability, an attacker must have valid administrative credentials.
- 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
Authenticator management requires secure distribution and handling of credentials, structurally discouraging hard-coded values.
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Cryptographic key management mandates proper establishment and handling instead of embedding keys in code.
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).
PR.AA-01's credential/key-management processes can reduce the incentive to embed secrets but do not address or detect hard-coded values in source code, so the weakness remains fully possible.
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).
PR.AA-02 addresses human identity proofing and per-person credential issuance at enrollment; it has no bearing on whether developers embed static credentials in software.
PR.DS-01 addresses encryption and integrity of stored data but never touches credential or key management practices, so it neither prevents hard-coded credentials nor removes any of their risk.
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.
Education on secure configuration practices discourages technical staff from embedding or relying on hard-coded credentials in systems and applications.
Secure key-generation, distribution and storage procedures reduce the likelihood that hard-coded or default cryptographic keys will be introduced or left unprotected.
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.