Emergence AI — agentic threat model
Emergence AI presents a high-risk profile due to its focus on autonomous multi-agent orchestration and direct integration with enterprise systems and tool control. While it claims a safety and security framework, the potential for cascading failures and unauthorized tool execution across complex workflows remains significant.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.60 |
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.
Utilizes fine-tuned LLMs and LVMs. Primary threats include adversarial prompt injection, model evasion, and potential data poisoning during the fine-tuning phase.
Not certain from the listing — The platform uses fine-tuned LLMs/LVMs, but specific data operations, vector stores, or RAG pipelines are not detailed.
Features Agent-Oriented Programming, planning, and tool control. Threats include insecure tool integration, unauthorized tool execution, and manipulation of the agent's planning logic.
Not certain from the listing — While it integrates into enterprise environments, the underlying hosting, sandboxing, and infrastructure security are not specified.
Not certain from the listing — A 'Safety and Security Framework' is mentioned, but specific evaluation, logging, or observability tools are not detailed.
Not certain from the listing — A 'Safety and Security Framework' is claimed, but specific compliance standards (e.g., SOC2, ISO) or identity/authorization controls are not explicitly defined.
Focuses heavily on Multi-Agent Systems and Intelligent Routing. Key threats include agent-to-agent trust abuse, rogue/compromised agents, and cascading failures across the multi-agent ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).