devflowinc/trieve — agentic threat model

Not certain from the listing — Trieve acts as an MCP server and retrieval pipeline, meaning the underlying foundation models are external and determined by the host client. The primary L1 threat is indirect prompt injection if the model processes untrusted crawled content retrieved by Trieve.

Highly critical layer for Trieve. Key threats include data/knowledge-base poisoning via the ingestion of malicious or manipulated web content during crawling, and embedding inversion or unauthorized data exfiltration if dataset access controls are bypassed.

The MCP framework orchestrates tool calling for crawling, chunking, and searching. Risks include tool misuse where an agent is manipulated into crawling malicious URLs, or memory/context poisoning within the host agent using retrieved search results.

Not certain from the listing — deployment details depend on how the MCP server is hosted (locally or cloud-based). Risks include exposure of Trieve API keys used to scope dataset access and lack of network sandboxing during crawl operations.

Not certain from the listing — there is no explicit mention of built-in evaluation, logging, or guardrails for the retrieved content, creating potential blind spots for detecting poisoned search results or injection attempts.

Security controls rely heavily on API keys to scope dataset access. Weaknesses in API key management or lack of granular role-based access control (RBAC) could lead to unauthorized dataset modification or retrieval.

As an MCP tool, Trieve is designed to be called by other agents. A compromised or rogue agent in a multi-agent ecosystem could abuse Trieve tools to exfiltrate proprietary datasets or flood the vector store with garbage data.