Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2026-26193 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Openwebui Open Webui. Its CVSS base score is 7.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 9th 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 LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
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-2026-26193 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting Open WebUI, a self-hosted artificial intelligence platform designed for offline operation. In versions prior to 0.6.44, attackers can manually modify chat history to set the `embeds` property on a response message. This property's content is then loaded into an iFrame with a sandbox attribute that includes `allow-scripts` and `allow-same-origin`, bypassing the "iframe Sandbox Allow Same Origin" configuration. The vulnerability impacts affected chats, including those in shared format.
The attack requires low privileges (PR:L), low attack complexity (AC:L), network access (AV:N), and user interaction (UI:R), with a CVSS v3.1 base score of 7.3 (C:H/I:H/A:N). An authenticated user with low privileges can craft a malicious payload by editing chat history, creating a shareable link that distributes the stored XSS payload to other users on the same instance. When victims access the shared chat or link, the payload executes in the sandboxed iFrame, potentially leading to high confidentiality and integrity impacts such as session hijacking, data theft, or further compromise within the user's browser context.
The GitHub security advisory (GHSA-vjm7-m4xh-7wrc) and related code changes confirm that updating to version 0.6.44 resolves the issue by addressing the improper iFrame sandbox handling. No additional mitigations are specified beyond applying the patch.
This vulnerability is notable in the context of self-hosted AI platforms, where shared chat links could propagate exploits across user bases in collaborative or multi-tenant deployments, though no public evidence of real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-7981
Vulnerability Data
Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Prior to version 0.6.44, aanually modifying chat history allows setting the `embeds` property on a response message, the content of which is loaded into an iFrame with…
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a sandbox that has `allow-scripts` and `allow-same-origin` set, ignoring the "iframe Sandbox Allow Same Origin" configuration. This enables stored XSS on the affected chat. This also triggers when the chat is in the shared format. The result is a shareable link containing the payload that can be distributed to any other users on the instance. Version 0.6.44 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: artificial intelligence, open webui
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.