Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:N/A:NSummary
CVE-2026-42366 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Geovision Gv-Lpc2011 Firmware. Its CVSS base score is 7.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 11th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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-2026-42366, published on 2026-05-04, describes multiple reflected cross-site scripting (XSS) vulnerabilities in the Web Interface / ssi.cgi functionality of GeoVision LPC2011/LPC2211 version 1.10. A specially crafted malicious URL can lead to arbitrary JavaScript code execution when processed by the affected component. The vulnerability carries a CVSS v3.1 base score of 7.4 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:N/A:N) and maps to CWE-79.
A remote attacker requires no privileges and can exploit this vulnerability over the network with low complexity by providing a crafted URL to a targeted user. Exploitation depends on user interaction, such as visiting the malicious URL in a browser, which triggers the reflected XSS payload. Successful execution runs arbitrary JavaScript in the victim's browser context, potentially leading to high confidentiality impacts like session hijacking or data exfiltration due to the changed scope (S:C).
Mitigation guidance and additional details are available in advisories from Talos Intelligence at https://talosintelligence.com/vulnerability_reports/ and GeoVision's cyber security page at https://www.geovision.com.tw/cyber_security.php.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-26857
Vulnerability Data
Multiple reflected cross-site scripting (xss) vulnerabilities exist in the Web Interface / ssi.cgi functionality of GeoVision LPC2011/LPC2211 1.10. A specially crafted malicious url can lead to an arbitrary javascript code execution. An attacker can provide a crafted URL to trigger…
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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.