← Ramparts Security Scanner MCP Server
Ramparts Security Scanner MCP Server — agentic threat model
Ramparts is a low-risk, deterministic security scanner designed to mitigate tool-poisoning and supply-chain risks in MCP environments. Its primary threat vector is parser exploitation or rule bypass rather than autonomous agentic failure.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.30 | |
| 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 — Ramparts is a static analysis and YARA tool, and may not directly use a foundation model itself, though it integrates into LLM/MCP agent workflows.
Not certain from the listing — It processes MCP tool definitions and YARA rules. Risks include poisoning of the YARA rule database or malicious tool definitions designed to exploit the parser.
Ramparts directly mitigates framework-level risks (tool-poisoning, insecure tool integration) by auditing MCP tool definitions before they are registered by the orchestrator.
Not certain from the listing — As an MCP server, it runs locally or in a container. If unsandboxed, parsing malicious tool definitions could lead to path traversal or remote code execution.
Acts as an observability and guardrail tool itself, flagging risky capability patterns and tool-poisoning attempts in the MCP ecosystem.
Provides compliance and security auditing for MCP-based agent deployments, helping enforce policies against unauthorized or dangerous tool capabilities.
Directly addresses ecosystem risks by preventing compromised or rogue MCP servers from introducing malicious tools into a multi-agent environment.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).