Raw vector
CVSS:3.1/AV:L/AC:L/PR:H/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2026-27009 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Openclaw Openclaw. Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 14th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SI-15 (Information Output Filtering) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8408
Vulnerability Data
OpenClaw is a personal AI assistant. Prior to version 2026.2.15, a atored XSS issue in the OpenClaw Control UI when rendering assistant identity (name/avatar) into an inline `<script>` tag without script-context-safe escaping. A crafted value containing `</script>` could break out…
more
of the script tag and execute attacker-controlled JavaScript in the Control UI origin. Version 2026.2.15 removed inline script injection and serve bootstrap config from a JSON endpoint and added a restrictive Content Security Policy for the Control UI (`script-src 'self'`, no inline scripts).
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS enables arbitrary JS execution in browser origin, directly facilitating browser session hijacking and web session cookie theft.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation and sanitization of untrusted input (assistant name/avatar) before it is rendered into HTML/script contexts, blocking the stored XSS payload.
Mandates output filtering/encoding of application data, which would have prevented the raw identity value from being emitted inside an inline <script> tag without context-safe escaping.
Requires malicious-code protection mechanisms (e.g., CSP enforcement or WAF rules) that can block execution of injected scripts even if input validation fails, matching the CSP added in the fix.
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.