← graphlit/graphlit-mcp-server
graphlit/graphlit-mcp-server — agentic threat model
The Graphlit MCP server presents a high-risk profile primarily due to its extensive integration with sensitive third-party data sources (Slack, GitHub, Google Drive) and its role as a centralized knowledge store. Compromise of this agent could lead to massive data exfiltration or widespread data poisoning across connected enterprise platforms.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.60 | |
| 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 specific foundation models used for semantic search, embedding generation, or retrieval-augmented generation are not disclosed, leaving model-specific vulnerabilities (like reprogramming or membership inference) unverified.
Critical risk area. Ingesting data from diverse sources like Slack, Discord, and GitHub exposes the vector store to data poisoning via malicious files or messages. Embedding inversion could also allow attackers to reconstruct sensitive ingested documents.
The orchestration of multiple connectors (Slack, Drive, GitHub) introduces risks of tool misuse or insecure tool integration if an MCP client can manipulate the server into unauthorized ingestion or data retrieval.
Not certain from the listing — the deployment environment, sandboxing of connectors, and secure storage of third-party credentials (API tokens for GitHub, Slack, Google Drive) are not detailed.
Not certain from the listing — there is no mention of observability, logging of retrieval queries, or guardrails to detect and block poisoned inputs during ingestion.
The server manages highly sensitive connector credentials and acts as a gateway to enterprise knowledge. Robust authentication and authorization controls are required to prevent unauthorized MCP clients from accessing the project store.
As an MCP server, this agent is designed to interact directly with external MCP clients. This creates a significant risk of agent-to-agent trust abuse, where a compromised client agent could query or poison the Graphlit knowledge base.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).