CVE-2023-4973
XSS in Creativeitem Academy Lms 6.2
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:N/I:L/A:NSummary
CVE-2023-4973 is a low-severity Cross-site Scripting (CWE-79) vulnerability in Creativeitem Academy Lms. Its CVSS base score is 3.5 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 23% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2023-4973 is a cross-site scripting vulnerability affecting Academy LMS version 6.2 running on Windows. It resides in the GET Parameter Handler of the /academy/tutor/filter endpoint, where unsanitized input to the searched_word, searched_tution_class_type[], searched_price_type[], and searched_duration[] parameters allows script injection.
An authenticated attacker with low privileges can supply a crafted GET request that executes arbitrary JavaScript in the browser of a victim user who follows the link, enabling theft of session tokens or other client-side actions. The attack requires user interaction and is rated 3.5 under CVSS 3.1.
Public references consist of a Packet Storm disclosure and corresponding Vuldb entries; the vendor was notified prior to publication but issued no response or patch. The associated EPSS score has remained low and essentially flat since disclosure, with a current value of 0.0491 and a peak of 0.0501.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-54809
Vulnerability Data
A vulnerability was found in Academy LMS 6.2 on Windows. It has been declared as problematic. Affected by this vulnerability is an unknown functionality of the file /academy/tutor/filter of the component GET Parameter Handler. The manipulation of the argument searched_word/searched_tution_class_type[]/searched_price_type[]/searched_duration[]…
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leads to cross site scripting. The attack can be launched remotely. The identifier VDB-239749 was assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.
- 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
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
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.