Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-68669 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in 5Ire 5Ire. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 34th 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 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-2025-68669 is a remote code execution (RCE) vulnerability affecting 5ire, a cross-platform desktop artificial intelligence assistant and model context protocol client. The issue resides in the useMarkdown.ts file in versions 0.15.2 and prior, where the markdown-it-mermaid plugin is initialized with securityLevel set to 'loose'. This configuration allows the rendering of HTML tags within Mermaid diagram nodes, enabling arbitrary code execution. The vulnerability is classified under CWE-79 (Cross-Site Scripting) with a CVSS v3.1 base score of 9.6 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H).
An attacker can exploit this vulnerability remotely without privileges by tricking a user into rendering malicious Markdown content containing a specially crafted Mermaid diagram. User interaction is required, such as opening or processing the Markdown in the 5ire application. Successful exploitation grants the attacker high confidentiality, integrity, and availability impacts with changed scope, potentially leading to full RCE on the victim's desktop system.
The GitHub security advisory (GHSA-5hpf-p8fw-j349) confirms the issue has not been patched as of publication on 2025-12-23. References point to the vulnerable code in useMarkdown.ts at line 156, a potential fix in commit 1fbe40d0bfbfe215370d45b9af856c286d67d3f2, and the v0.15.2 release, but no official patch is available in the affected versions.
As a desktop AI assistant, 5ire's vulnerability carries relevance to AI/ML workflows where users process Markdown from untrusted sources, such as shared model contexts or documentation. No real-world exploitation has been reported at the time of publication.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-205025
Vulnerability Data
5ire is a cross-platform desktop artificial intelligence assistant and model context protocol client. In versions 0.15.2 and prior, an RCE vulnerability exists in useMarkdown.ts, where the markdown-it-mermaid plugin is initialized with securityLevel: 'loose'. This configuration explicitly permits the rendering of…
more
HTML tags within Mermaid diagram nodes. This issue has not been patched at time of publication.
- 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: artificial intelligence, model context protocol
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
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.