Multi-Agent Orchestrator — agentic threat model

Not certain from the listing — The framework is model-agnostic and relies on external LLMs for intent classification and agent responses. Adversarial prompt injections could manipulate the classifier to misroute queries or trigger unintended agent behaviors.

Not certain from the listing — While context management is highlighted, the underlying storage mechanisms (databases, vector stores) are not specified. Risks include context/state poisoning and unauthorized data access across agent boundaries.

As an orchestration framework, vulnerabilities in the Python/TypeScript routing logic or context management code could allow attackers to bypass intent classifiers, hijack agent execution flows, or inject malicious payloads into the shared context.

Supports deployment on AWS Lambda, local, and cloud platforms. Security risks include insecure AWS IAM configurations, lack of container isolation between different orchestrated agents, and exposure of API keys or environment variables.

Not certain from the listing — No explicit mention of built-in guardrails, evaluation frameworks, or logging mechanisms. This creates a blind spot where malicious routing or agent exploitation could go undetected.

Not certain from the listing — The description does not detail access control policies, user authentication, or compliance frameworks governing how different agents or users interact with the orchestrator.

Highly critical layer for this framework. The multi-agent architecture is susceptible to Agent-to-Agent (A2A) trust abuse, where a single compromised or rogue agent can poison the shared conversational context and cause cascading failures across all other integrated agents.