Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2022-2733 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Open-Emr Openemr. Its CVSS base score is 6.1 (Medium).
Operationally, ranked in the top 0.1% of CVEs by exploit likelihood; 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 SI-10 (Information Input Validation) and SI-15 (Information Output Filtering) — 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-2022-2733 is a reflected cross-site scripting vulnerability (CWE-79) affecting the OpenEMR electronic health records platform in versions prior to 7.0.0.1. The flaw resides in the web application code and carries a CVSS 3.1 score of 6.1, reflecting network attack vector, low complexity, no required privileges, and required user interaction with changed scope.
An unauthenticated remote attacker can exploit the issue by crafting a malicious link that, when clicked by a victim, executes arbitrary script in the context of the OpenEMR application. Successful exploitation allows limited disclosure and modification of data within the affected user's session while potentially impacting other users or resources due to the changed scope.
The referenced commit 59458bc15ab0cb556c521de9d5187167d6f88945 in the OpenEMR repository addresses the vulnerability, and the associated huntr.dev report confirms the fix is included in release 7.0.0.1. The EPSS score has remained elevated, with a current value of 0.8967 and a recorded peak of 0.9175.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-34975
Vulnerability Data
Cross-site Scripting (XSS) - Reflected in GitHub repository openemr/openemr prior to 7.0.0.1.
- CWE(s)
Related Threats
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation of all inputs (URL parameters, query strings) to reject or sanitize untrusted data that would otherwise be reflected and executed as script.
Requires filtering or encoding of information returned to users, preventing malicious script in reflected responses from executing in the victim's browser context.
Enables monitoring of web requests and anomalous reflected content patterns that indicate attempted XSS exploitation against the OpenEMR application.
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.