Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:L/SC:L/SI:L/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:Y/R:U/V:X/RE:M/U:ClearSummary
CVE-2026-1695 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Arcinfo Pcvue. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Drive-by Compromise (T1189); ranked at the 11th 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-2026-8839
Vulnerability Data
An XSS vulnerability affects the OAuth web services used by the WebVue, WebScheduler, TouchVue and SnapVue features of PcVue in version 12.0.0 through 16.3.3 included. It might allow a remote attacker to trick a legitimate user into loading content from…
more
another site upon unsuccessful user authentication on an unknown application (unknown client_id). This vulnerability only affects the error page of the OAuth server.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
XSS on public OAuth error page enables drive-by script execution via malicious links and direct exploitation of a public-facing web service.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Validates untrusted parameters such as client_id on the OAuth error page before they are reflected into HTML, directly blocking the CWE-79 XSS vector.
Filters or encodes dynamic content written to the OAuth error response, preventing injected scripts from executing in the victim's browser.
Ensures error pages generated by the OAuth service do not unsafely incorporate request data, limiting the attack surface described in the CVE.
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.