New Relic MCP Server — agentic threat model
The New Relic MCP Server acts as a high-value bridge to sensitive telemetry, logs, and system entities, presenting significant data exposure risks if compromised, though bounded by NRQL query permissions.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.40 |
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 MCP server itself acts as a tool provider rather than hosting the foundation model; model-level vulnerabilities depend entirely on the external orchestrating agent.
Exposes highly sensitive operational telemetry, logs, and traces via NRQL. Risks include data exfiltration of proprietary system configurations, PII in logs, and metadata leakage through unauthorized queries.
Integrates as an MCP tool. Vulnerabilities include insecure tool execution where an orchestrating agent could be manipulated via prompt injection to run destructive or overly broad NRQL queries.
Operates as a remote endpoint. Risks include unauthorized access to the endpoint, lack of transport encryption, or exposure of the host environment running the MCP server.
While the tool itself is designed for observability, there is a risk of insufficient logging of the agent's own queries, leading to blind spots regarding which agent executed which NRQL command.
Relies on account scope and query permissions to restrict access. Security posture depends heavily on enforcing least-privilege API keys and robust IAM policies to prevent privilege escalation.
Designed to be called by other agents in an ecosystem. Threat of cascading failures or trust abuse if a compromised upstream agent uses this tool to map the entire infrastructure topology.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).