Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:NSummary
CVE-2026-39974 is a high-severity SSRF (CWE-918) vulnerability in N8N-Mcp N8N-Mcp. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 24th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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 AC-4 (Information Flow Enforcement) 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-2026-39974 is an authenticated Server-Side Request Forgery (SSRF) vulnerability, mapped to CWE-918, affecting n8n-MCP versions prior to 2.47.4. n8n-MCP is a Model Context Protocol (MCP) server designed to provide AI assistants with access to n8n node documentation, properties, and operations. The issue arises when a caller with a valid AUTH_TOKEN supplies arbitrary URLs through multi-tenant HTTP headers, tricking the server into issuing HTTP requests to those destinations and reflecting the response bodies back via JSON-RPC.
Exploitation targets multi-tenant HTTP deployments where multiple operators can use valid AUTH_TOKENs or tokens are shared with less-trusted clients. An authenticated attacker can read contents from any URL reachable by the server process, including cloud instance metadata endpoints such as AWS IMDS, GCP, Azure, Alibaba, and Oracle, as well as internal network services. Single-tenant stdio deployments and HTTP setups without multi-tenant headers remain unaffected. The vulnerability carries a CVSS v3.1 base score of 8.5 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:N), highlighting high confidentiality impact across a scoped network.
Mitigation is available by upgrading to n8n-MCP version 2.47.4, which addresses the flaw. Official details are provided in the GitHub security advisory (GHSA-4ggg-h7ph-26qr), release notes for v2.47.4, and the fixing commit (d9d847f230923d96e0857ccecf3a4dedcc9b0096).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-20968
Vulnerability Data
n8n-MCP is a Model Context Protocol (MCP) server that provides AI assistants with comprehensive access to n8n node documentation, properties, and operations. Prior to 2.47.4, an authenticated Server-Side Request Forgery in n8n-mcp allows a caller holding a valid AUTH_TOKEN to…
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cause the server to issue HTTP requests to arbitrary URLs supplied through multi-tenant HTTP headers. Response bodies are reflected back through JSON-RPC, so an attacker can read the contents of any URL the server can reach — including cloud instance metadata endpoints (AWS IMDS, GCP, Azure, Alibaba, Oracle), internal network services, and any other host the server process has network access to. The primary at-risk deployments are multi-tenant HTTP installations where more than one operator can present a valid AUTH_TOKEN, or where a token is shared with less-trusted clients. Single-tenant stdio deployments and HTTP deployments without multi-tenant headers are not affected. This vulnerability is fixed in 2.47.4.
- 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: ai, 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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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.