Secure Fetch MCP — agentic threat model
Secure Fetch MCP is a low-risk, single-purpose utility agent designed specifically to mitigate SSRF risks by restricting outbound HTTP requests to public IP ranges. Its lack of autonomy, planning, or stateful memory makes its overall agentic risk profile exceptionally low.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.10 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.10 | |
| Opacity & Reflexivity | 0.10 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — The agent is an MCP tool and does not specify a native foundation model. It is designed to be called by external LLMs, which themselves remain vulnerable to prompt injection or adversarial bypass attempts targeting the fetch parameters.
Not certain from the listing — The tool does not manage training data, vector databases, or RAG pipelines. It acts strictly as a transient network fetcher, meaning data operations risks are minimal to non-existent.
This tool directly addresses L3 vulnerabilities by acting as a secure, restricted tool integration. It mitigates tool misuse (specifically SSRF) by preventing an orchestrating agent framework from accessing local or internal network resources.
The tool operates at the network boundary of the deployment infrastructure. By blocking access to private IP ranges (RFC 1918) and cloud metadata endpoints (e.g., 169.254.169.254), it prevents lateral movement and host-level credential theft.
Not certain from the listing — The description does not specify its logging, auditing, or anomaly detection capabilities for blocked connection attempts, which are critical for detecting active SSRF exploitation attempts.
The tool implements a hard security policy control (SSRF guardrails) to enforce network compliance, ensuring that any agent using this tool cannot violate organizational boundaries regarding internal data access.
In a multi-agent ecosystem, this tool prevents cascading failures or trust abuse where a compromised or rogue agent might attempt to use another agent's fetch capabilities to scan or exploit internal services.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).