Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2023-5863 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Phpmyfaq Phpmyfaq. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 37% 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-2023-5863 is a reflected cross-site scripting vulnerability affecting the thorsten/phpmyfaq GitHub repository in versions prior to 3.2.2. The flaw, classified under CWE-79, permits injection of malicious scripts that execute in the victim's browser context, carrying a CVSS 3.1 score of 6.1 due to its network attack vector, low complexity, lack of required privileges, and changed scope with limited confidentiality and integrity impact.
An unauthenticated remote attacker can exploit the issue by crafting a malicious link or request that reflects attacker-controlled input back to a user who clicks it, resulting in script execution within the phpMyFAQ application domain. Successful exploitation allows limited data exposure or manipulation actions scoped to the affected user's session without broader system compromise.
Public references point to a fix merged in commit 97e813dcd2022bd10a8770569a8b02591716365f, which addresses the reflected XSS vector; administrators are advised to upgrade phpMyFAQ to version 3.2.2 or later. The associated huntr.com bounty entry documents the same remediation path.
EPSS values remain low with only minor fluctuation between the recorded peak of 0.0754 and current score of 0.0622, providing no indication of emerging exploitation interest.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-2756
Vulnerability Data
Cross-site Scripting (XSS) - Reflected in GitHub repository thorsten/phpmyfaq prior to 3.2.2.
- 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.