Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2026-42282 is a medium-severity Insertion of Sensitive Information into Log File (CWE-532) vulnerability in N8N-Mcp N8N-Mcp. Its CVSS base score is 4.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Unsecured Credentials (T1552); ranked at the 17th 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 Privacy and Disclosure risk domain.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-28824
Vulnerability Data
n8n-MCP is an MCP server that provides AI assistants access to n8n node documentation, properties, and operations. Prior to version 2.47.13, when n8n-mcp runs in HTTP transport mode, authenticated MCP tools/call requests had their full arguments and JSON-RPC params written…
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to server logs by the request dispatcher and several sibling code paths before any redaction. When a tool call carries credential material — most notably n8n_manage_credentials.data — the raw values can be persisted in logs. In deployments where logs are collected, forwarded to external systems, or viewable outside the request trust boundary (shared log storage, SIEM pipelines, support/ops access), this can result in disclosure of: bearer tokens and OAuth credentials sent through n8n_manage_credentials, per-tenant API keys and webhook auth headers embedded in tool arguments, arbitrary secret-bearing payloads passed to any MCP tool. The issue requires authentication (AUTH_TOKEN accepted by the server), so unauthenticated callers cannot trigger it; the runtime exposure is also reduced by an existing console-silencing layer in HTTP mode, but that layer is fragile and the values are still constructed and passed into the logger. This issue has been patched in version 2.47.13.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, mcp
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Procedures mandate excluding sensitive data from logs to prevent unauthorized exposure via audit records.
Identifies insertion of sensitive data into logs, allowing detection of unauthorized disclosure.
Cross-organizational coordination enables agreement on what data to include in audit logs, directly reducing insertion of sensitive information.
Identifying logging as a data action allows prevention of sensitive information being inserted into log files.
The process of identifying and eradicating spilled information applies directly to sensitive data inserted into log files.
Specific processing rules for sensitive PII categories commonly include restrictions on logging, making insertion of such data into log files less likely.
PIAs detect planned or existing logging of PII and require removal or protection, preventing insertion of sensitive information into logs.
Limits insertion of sensitive operational details into logs by treating such data as key information requiring protection.
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 prohibit writing sensitive data to logs; eliminating the weakness satisfies only one narrow slice of the control.
Log generation configuration can and should exclude sensitive data, but the control statement focuses on availability rather than content filtering.
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.
Requiring de-identification and privacy controls before logs leave the organization reduces the chance that sensitive data inadvertently captured in logs becomes exposed to external parties.
By defining what records must be kept, where, and for how long, the control discourages the inadvertent inclusion of sensitive information in logs or other externally accessible files that fall outside the formal record system.
Mandating deletion of temporary files and logs that may contain sensitive information prevents those artifacts from remaining accessible after the data is no longer needed.
When log entries are produced from masked data sets, the control prevents the inadvertent insertion of sensitive values into externally accessible log files.
DLP inspection of logs and file transfers can detect and block the inadvertent placement of sensitive tokens or credentials into externally accessible log files before they are written or transmitted.
Requiring restrictions on free-text fields and proper error-message handling stops developers from embedding or leaking sensitive data into logs or diagnostic output.