← CircleCI/mcp-server-circleci
CircleCI/mcp-server-circleci — agentic threat model
This agent poses a high security risk due to its access to sensitive CI/CD pipelines, build logs, and CircleCI tokens, which could be abused via prompt injection to execute supply chain attacks or exfiltrate secrets.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| 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 | 0.40 | |
| Opacity & Reflexivity | 0.50 |
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 listing does not specify the underlying LLM used by the agent, but standard threats like prompt injection could lead to unauthorized tool execution or token exfiltration.
Not certain from the listing — The agent accesses build logs and pipeline data, but details on vector stores or RAG data operations are not provided.
The agent uses the Model Context Protocol (MCP) to integrate with CircleCI, exposing tools for pipeline/job data access, log retrieval, and potentially triggering builds. Threats include tool misuse (e.g., triggering unauthorized builds or exfiltrating logs/tokens).
Not certain from the listing — The deployment environment of the MCP server and how CircleCI tokens are securely stored/sandboxed is not detailed.
Not certain from the listing — No specific monitoring, logging, or guardrails for the MCP server are mentioned in the directory listing.
The agent handles highly sensitive CircleCI tokens and build logs. Access control, token management, and authorization policies are critical to prevent unauthorized pipeline execution or data exposure.
The agent is designed to be called by other AI agents via the MCP protocol, introducing risks of multi-agent trust abuse, where a compromised upstream agent could abuse this tool to trigger malicious builds.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).