CVE-2023-2120
XSS in I13Websolution Thumbnail Carousel Slider ≤ 1.1.9
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2023-2120 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in I13Websolution Thumbnail Carousel Slider. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 46th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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 Thumbnail carousel slider plugin for WordPress, also known as wp-responsive-thumbnail-slider, is affected by a reflected cross-site scripting vulnerability in versions up to and including 1.1.9. The flaw, tracked as CWE-79, arises from insufficient input sanitization and output escaping on the search_term parameter and carries a CVSS 3.1 score of 6.1.
Unauthenticated attackers can exploit the issue by supplying a malicious payload in the search_term parameter and then tricking an authenticated user into clicking a crafted link. Successful exploitation allows arbitrary script execution in the victim's browser with limited impacts on confidentiality and integrity under the reflected XSS model.
References from Wordfence and the WordPress plugin repository show that the vulnerability was resolved in version 1.1.10, with the fix implemented in the wp-responsive-images-thumbnail-slider.php file as documented in the associated changeset. The EPSS score rose from lower values after disclosure to a peak of 0.0633 before receding to the current 0.0368.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-33641
Vulnerability Data
The Thumbnail carousel slider plugin for WordPress is vulnerable to Reflected Cross-Site Scripting via the search_term parameter in versions up to, and including, 1.1.9 due to insufficient input sanitization and output escaping. This makes it possible for unauthenticated attackers to…
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inject arbitrary web scripts in pages that execute if they can successfully trick a user into performing an action such as clicking on a link.
- 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.