Raw vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-32121 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Open-Emr Openemr. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 9th 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-2026-32121 is a stored cross-site scripting (XSS) vulnerability affecting OpenEMR, a free and open-source electronic health records and medical practice management application, in versions prior to 8.0.0.1. The issue arises from unsanitized patient names stored in the patient_data table, enabling server-side rendering of malicious content via raw PHP echo in the prescription CSS/HTML print view. A related but distinct client-side DOM-based XSS occurs via jQuery .html() in the portal/sign/assets/signer_api.js component. These share the same root cause but involve different sinks, affected components, trigger actions, and fixes.
The vulnerability has a CVSS v3.1 base score of 7.7 (AV:N/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:N), indicating exploitation over the network by an attacker with low privileges, such as an authenticated user like a patient or staff member capable of modifying demographics. High attack complexity and required user interaction mean the attacker must craft a payload in a patient name, which a victim (e.g., a clinician printing prescriptions or using the signer API) then triggers by viewing the affected content. Successful exploitation allows high confidentiality and integrity impacts with changed scope, potentially enabling theft of session cookies, data exfiltration, or unauthorized modifications within the victim's browser context.
The vulnerability is addressed in OpenEMR 8.0.0.1, which implements independent fixes for the server-side and client-side issues. Additional mitigation details are available in the GitHub Security Advisory at https://github.com/openemr/openemr/security/advisories/GHSA-68fr-xm3v-p4vw.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-11389
Vulnerability Data
OpenEMR is a free and open source electronic health records and medical practice management application. Prior to 8.0.0.1, Stored XSS in prescription CSS/HTML print view via patient demographics. That finding involves server-side rendering of patient names via raw PHP echo.…
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This finding involves client-side DOM-based rendering via jQuery .html() in a completely different component (portal/sign/assets/signer_api.js). The two share the same root cause (unsanitized patient names in patient_data), but they have different sinks, different affected components, different trigger actions, and require independent fixes. This vulnerability is fixed in 8.0.0.1.
- 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.