Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-39885 is a high-severity SSRF (CWE-918) vulnerability in Agentfront \@Frontmcp\/Adapters. Its CVSS base score is 7.5 (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; 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 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-2026-39885 affects FrontMCP, a TypeScript-first framework for the Model Context Protocol (MCP), specifically in versions prior to 2.3.0. The vulnerability resides in the mcp-from-openapi library, which relies on @apidevtools/json-schema-ref-parser to dereference $ref pointers in OpenAPI specifications. Without URL restrictions or custom resolvers, this parser fetches resources referenced by $ref values during the initialize() call. Malicious OpenAPI specifications can point to internal network addresses, cloud metadata endpoints, or local files, enabling server-side request forgery (SSRF) and local file read attacks when processing untrusted inputs. The issue is classified under CWE-918 (Server-Side Request Forgery) with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N).
Remote attackers require no privileges or user interaction to exploit this vulnerability by supplying a crafted OpenAPI specification to a vulnerable FrontMCP instance. During initialization, the library will attempt to resolve malicious $ref pointers, allowing attackers to force the server to make unauthorized requests to internal networks, access cloud instance metadata services, or read local files. This results in high confidentiality impacts, such as exfiltration of sensitive internal data or metadata that could facilitate further compromise.
The vulnerability is fixed in FrontMCP version 2.3.0. Security practitioners should upgrade to this version or later. Additional details are available in the GitHub security advisory at GHSA-v6ph-xcq9-qxxj and related release notes.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-20632
Vulnerability Data
FrontMCP is a TypeScript-first framework for the Model Context Protocol (MCP). Prior to 2.3.0, the mcp-from-openapi library uses @apidevtools/json-schema-ref-parser to dereference $ref pointers in OpenAPI specifications without configuring any URL restrictions or custom resolvers. A malicious OpenAPI specification containing $ref…
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values pointing to internal network addresses, cloud metadata endpoints, or local files will cause the library to fetch those resources during the initialize() call. This enables Server-Side Request Forgery (SSRF) and local file read attacks when processing untrusted OpenAPI specifications. This vulnerability is fixed in 2.3.0.
- 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: 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.