OpenAI Swarm — agentic threat model
OpenAI Swarm is an educational, stateless multi-agent orchestration framework with high inherent risk if deployed in production due to its lack of built-in security controls, sandboxing, or state persistence, relying entirely on client-side implementation safety.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| 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 — Swarm is model-agnostic but typically relies on OpenAI chat completion models. It inherits standard LLM vulnerabilities like prompt injection, which could manipulate agent handoff logic.
Swarm is explicitly stateless between runs and client-side, meaning it does not manage persistent databases or vector stores natively, reducing data-at-rest poisoning risks.
The framework relies heavily on function calling for agent handoffs and tool execution. Insecure tool integration or malicious inputs could lead to arbitrary code execution on the client side.
As a client-side SDK, Swarm does not provide built-in sandboxing, containerization, or secrets management, leaving infrastructure security entirely up to the developer's local environment.
Designed as an educational SDK, Swarm lacks production-grade evaluation, guardrails, or observability tools, creating significant blind spots if used in real-world scenarios.
There are no built-in authentication, authorization, or compliance controls. The framework is explicitly not intended for production and lacks enterprise security alignment.
The core multi-agent orchestration and handoff mechanism introduces risks of cascading failures, infinite loops of agent transfers, and trust abuse between different agent personas.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.