Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2023-39002 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Opnsense Opnsense. 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 34% 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.
The vulnerability CVE-2023-39002 is a cross-site scripting flaw in the act parameter of system_certmanager.php, affecting OPNsense Community Edition versions before 23.7 and Business Edition versions before 23.4.2. Classified as CWE-79 with a CVSS 3.1 score of 6.1, the issue stems from insufficient sanitization that allows arbitrary web scripts or HTML to be injected and rendered via a crafted payload.
Attackers without authentication can exploit the flaw by delivering a malicious payload that executes in a victim's browser when the parameter is processed. Because the attack vector requires user interaction and results in changed scope, successful exploitation can achieve limited impacts on confidentiality and integrity, such as script execution that alters page content or accesses session data within the OPNsense web interface.
The referenced OPNsense core commit a4f6a8f8d604271f81984cfcbba0471af58e34dc implements the fix for the input handling defect, and administrators are advised to upgrade to the patched releases. The Logical Trust analysis at the provided URL details the discovery and confirms that the vulnerability is resolved by applying these updates. The associated EPSS score has remained flat at its peak value of 0.2358 with no material increase observed.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-42759
Vulnerability Data
A cross-site scripting (XSS) vulnerability in the act parameter of system_certmanager.php in OPNsense Community Edition before 23.7 and Business Edition before 23.4.2 allows attackers to execute arbitrary web scripts or HTML via a crafted payload.
- 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.