Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:L/I:L/A:LSummary
CVE-2026-25904 is a medium-severity SSRF (CWE-918) vulnerability in Jfrog (inferred from references). Its CVSS base score is 5.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 6th 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 SC-7 (Boundary Protection) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-6603
Vulnerability Data
The Pydantic-AI MCP Run Python tool configures the Deno sandbox with an overly permissive configuration that allows the underlying Python code to access the localhost interface of the host to perform SSRF attacks. Note - the "mcp-run-python" project is archived…
more
and unlikely to receive a fix.
- 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
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
SSRF weakness (CWE-918) in exposed sandboxed tool directly enables exploitation of a public-facing application for initial or chained access.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly blocks the SSRF vector by enforcing network boundary controls that deny sandbox-initiated connections to the host localhost interface.
Enforces information flow policies that can explicitly prohibit the Python code inside the Deno sandbox from reaching host-local addresses.
Requires configuring the Deno sandbox (and any equivalent execution environment) with only the minimal network capabilities needed, eliminating the permissive localhost exposure.
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.