Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:NSummary
CVE-2026-22154 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Fortinet Fortisoar. Its CVSS base score is 4.6 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); 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-2026-22317
Vulnerability Data
An improper neutralization of input during web page generation ('cross-site scripting') vulnerability in Fortinet FortiSOAR PaaS 7.6.0 through 7.6.3, FortiSOAR PaaS 7.5.0 through 7.5.2, FortiSOAR PaaS 7.4 all versions, FortiSOAR PaaS 7.3 all versions, FortiSOAR on-premise 7.6.0 through 7.6.3, FortiSOAR…
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on-premise 7.5.0 through 7.5.2, FortiSOAR on-premise 7.4 all versions, FortiSOAR on-premise 7.3 all versions may allow an authenticated remote attacker to perform a stored cross site scripting (XSS) attack via crafted HTTP Requests.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS enables browser session hijacking and theft of web session cookies from authenticated users viewing injected content.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation and sanitization of all HTTP input to block crafted payloads that produce stored XSS.
Requires filtering of information returned in web responses, mitigating unneutralized script content before it reaches victims.
Enables integrity checks and monitoring that can detect unauthorized script insertion or modification in stored web content.
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.