multi-agent-patterns
Pass
Audited by Gen Agent Trust Hub on Sep 14, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONEXTERNAL_DOWNLOADS
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill defines architectures that ingest untrusted inputs (e.g., researcher tasks, user queries) and provides a
FileSystemCoordinationutility in the technical reference that reads and writes JSON state files to a shared workspace. This architecture presents a surface for indirect prompt injection where malicious data processed by one agent could influence the behavior of others via the shared filesystem or coordination layer.\n - Ingestion points: Task descriptions and worker outputs processed by the
SupervisorAgentinscripts/coordination.pyand LangGraph state objects inreferences/frameworks.md.\n - Boundary markers:
SKILL.mdcontains specific instructions to use explicit coordination protocols and validation steps to prevent information degradation and error propagation.\n - Capability inventory:
scripts/coordination.pymanages inter-agent message passing;references/frameworks.mdincludesFileSystemCoordinationandCheckpointManagerclasses that utilizejsonandosmodules for persistent storage of agent state.\n - Sanitization: The skill recommends implementing verification agents to cross-check critical outputs and validating all worker responses before they are aggregated or passed downstream.\n- [EXTERNAL_DOWNLOADS]: The skill provides links to the official documentation and research papers for established multi-agent frameworks, including LangGraph, AutoGen, and CrewAI. These resources originate from well-known technology organizations and reputable academic sources.
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