Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-66580 is a critical-severity Code Injection (CWE-94) vulnerability in Openagentplatform Dive. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 40th 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 AI Agent Protocols and Integrations; in the Protocol-Specific Risks 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.
Dive is an open-source Model Context Protocol (MCP) Host Desktop Application designed to enable integration with function-calling large language models (LLMs). A critical Stored Cross-Site Scripting (XSS) vulnerability, tracked as CVE-2025-66580, affects versions prior to 0.11.1 in its Mermaid diagram rendering component. This flaw allows the execution of arbitrary JavaScript code via javascript: protocols, as mapped to CWE-79 (XSS) and CWE-94 (code injection).
Attackers can exploit the vulnerability remotely without privileges by injecting a malicious MCP server configuration into the application. Exploitation requires user interaction, specifically clicking on the affected node in the diagram, which triggers the payload and results in remote code execution (RCE) on the victim's machine. The issue carries 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), reflecting high impacts on confidentiality, integrity, and availability with low attack complexity over the network.
The official GitHub security advisory (GHSA-xv8m-365j-x6h2) for the OpenAgentPlatform/Dive repository confirms that updating to version 0.11.1 resolves the vulnerability by addressing the javascript: execution in the Mermaid renderer.
Notably, the vulnerability occurs in a desktop application tailored for LLM integrations, underscoring security risks in emerging AI-agent tools that handle dynamic content like diagrams. No public reports of real-world exploitation were available as of the CVE publication on 2025-12-19.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-204564
Vulnerability Data
Dive is an open-source MCP Host Desktop Application that enables integration with function-calling LLMs. A critical Stored Cross-Site Scripting (XSS) vulnerability exists in versions prior to 0.11.1 in the Mermaid diagram rendering component. The application allows the execution of arbitrary…
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JavaScript via `javascript:`. An attacker can exploit this to inject a malicious Model Context Protocol (MCP) server configuration, leading to Remote Code Execution (RCE) on the victim's machine when the node is clicked. Version 0.11.1 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Protocol-Specific Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: llms, mcp, model context protocol
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics 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).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.