Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2026-33346 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Open-Emr Openemr. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 25th 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-2026-33346 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting OpenEMR, a free and open source electronic health records and medical practice management application. Versions prior to 8.0.0.2 are vulnerable in the patient portal payment flow, where arbitrary JavaScript payloads submitted by users are stored via portal/lib/paylib.php and rendered without proper escaping in portal/portal_payment.php. The issue carries a CVSS v3.1 base score of 8.7 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N), indicating high severity due to its potential for significant confidentiality and integrity impacts.
An authenticated patient portal user with low privileges (PR:L) can exploit this vulnerability over the network (AV:N) by injecting malicious JavaScript during payment submission. The payload persists in storage and executes in the browser of a staff member (UI:R) who later reviews the payment details in portal/portal_payment.php, with low attack complexity (AC:L). Successful exploitation enables theft of sensitive data or manipulation of staff actions in the victim's session, achieving high confidentiality and integrity impacts (C:H/I:H) across a changed scope (S:C) without affecting availability.
OpenEMR version 8.0.0.2 addresses the vulnerability through a fix detailed in the commit at https://github.com/openemr/openemr/commit/6e9e1566d6e271a6d839614674b887e3a73d7da1. Additional mitigation guidance is available in the GitHub Security Advisory at https://github.com/openemr/openemr/security/advisories/GHSA-qvf6-6xc6-9qv7. Security practitioners should prioritize upgrading affected instances and reviewing patient portal configurations for exposure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-13229
Vulnerability Data
OpenEMR is a free and open source electronic health records and medical practice management application. Prior to 8.0.0.2, a stored cross-site scripting (XSS) vulnerability in the patient portal payment flow allows a patient portal user to persist arbitrary JavaScript that…
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executes in the browser of a staff member who reviews the payment submission. The payload is stored via `portal/lib/paylib.php` and rendered without escaping in `portal/portal_payment.php`. Version 8.0.0.2 fixes the issue.
- 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.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.