Stored XSS in GitLab Analytics Dashboards Enables Session Hijacking

GitLab has remediated an issue in GitLab EE affecting all versions from 18.7 before 18.9.7, 18.10 before 18.10.6, and 18.11 before 18.11.3 that, in customizable analytics dashboards, could have allowed an authenticated user to execute arbitrary JavaScript in the context of other users' browsers due to improper input sanitization.

Casky was already ahead

This CVE exploits attack patterns that Casky's 301matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

GitLab EE versions 18.7 through 18.11 contain a stored cross-site scripting (XSS) vulnerability in customizable analytics dashboards that allows authenticated attackers to inject and execute arbitrary JavaScript in other users' browsers. This CWE-79 vulnerability matters because it breaks the fundamental trust model of collaborative platforms—attackers can steal session tokens, exfiltrate sensitive data, modify dashboard configurations, or pivot to administrative accounts without additional credential compromise. Organizations using affected GitLab EE versions with shared analytics dashboards face immediate risk, particularly those with multi-tenant environments or teams handling sensitive project metrics.

Casky.ai's 301 mapped skills detect the attack chain behind this vulnerability by analyzing MITRE ATT&CK techniques TA0001 (Initial Access through authenticated sessions) and TA0043 (Reconnaissance of dashboard input fields). Claude's extended reasoning identifies the critical pattern: improper input sanitization in dashboard customization endpoints creates opportunities for payload injection. Practitioners using Casky would observe findings correlating stored payload delivery mechanisms, DOM-based execution contexts, and cross-user script propagation—flagging POST requests to dashboard endpoints with unescaped HTML/JavaScript, absence of Content Security Policy headers, and suspicious script tags in analytics configuration parameters. The skill set further correlates session management anomalies with script execution timelines, helping teams distinguish this stored XSS from reflected variants and prioritize remediation of the input validation flaws in versions 18.9.7+, 18.10.6+, and 18.11.3+.

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Casky Risk Score

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EPSS Probability

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Shodan Exposure

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GreyNoise Signal

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301 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-7377.

analyzing-apt-group-with-mitre-navigator

threat intelligence · low

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analyzing-campaign-attribution-evidence

threat intelligence · low

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analyzing-certificate-transparency-for-phishing

threat intelligence · low

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MITRE ATT&CK Techniques

TA0001 TA0043

Investigate the attack patterns behind CVE-2026-7377

Casky has 301 skills that investigate the attack patterns behind CVE-2026-7377. Run one and get CVSS-scored findings in 3 minutes.

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