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duaraghav8/MCPJungle — agentic threat model

7.7AIVSS 7.7 · High

MCPJungle acts as a central registry and proxy gateway for Model Context Protocol (MCP) servers, concentrating access to enterprise tools and credentials. Its primary risk is serving as a single point of compromise that could expose multiple downstream tools and sensitive enterprise systems to rogue AI agents.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.61Factor sum 3.9/10Threat ×1.05Mitigation ×0.85
Autonomy of Action
0.20
Goal-Driven Planning
0.10
Self-Modification
0.00
Dynamic Tool Use
0.90
Persistent Memory
0.30
Contextual Awareness
0.40
Dynamic Identity
0.80
Multi-Agent Interactions
0.70
Non-Determinism
0.20
Opacity & Reflexivity
0.30

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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — MCPJungle is a registry and proxy gateway rather than an LLM provider. It does not host foundation models directly, but model-reprogramming or adversarial prompts passed through it could exploit downstream tools.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — The registry catalogs servers but does not explicitly manage RAG data or vector stores, though the proxied MCP servers themselves may connect to enterprise databases.

L3 · Agent Frameworks✓ mapped

MCPJungle directly impacts agent frameworks by acting as the central discovery and proxy layer for tool integration. Vulnerabilities here could allow agents to discover unauthorized tools or execute insecure tool calls.

L4 · Deployment & Infrastructure✓ mapped

As a self-hosted Go implementation, deployment security is critical. Compromise of the host or container running MCPJungle could expose central credentials used to authenticate with registered MCP servers.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — The description does not detail built-in logging, guardrails, or anomaly detection for the proxied tool calls, creating potential observability blind spots.

L6 · Security & Compliance (cross-cutting)✓ mapped

This is a core focus of MCPJungle, which provides enterprise access control and central credential handling. Weaknesses in its authentication or authorization logic would undermine the security of all connected MCP servers.

L7 · Agent Ecosystem✓ mapped

MCPJungle sits at the heart of the agent ecosystem, facilitating multi-agent tool discovery. A compromised registry could distribute malicious MCP servers, leading to cascading failures across the enterprise agent network.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).