Miovision Adaptive — agentic threat model

Not certain from the listing — The specific foundation models or neural networks driving the adaptive traffic decisions are not disclosed. Threats include adversarial manipulation of visual or telemetry inputs to force incorrect traffic light states.

Not certain from the listing — The data pipeline for real-time traffic sensors and historical flow databases is unspecified. Threats include data poisoning of traffic flow baselines, leading to artificial congestion or emergency vehicle preemption abuse.

Not certain from the listing — The orchestration framework managing the decision-making loop is not detailed. Threats include insecure tool integration where the agent issues unsafe physical signal phase commands without hardware-level safety interlocks.

Not certain from the listing — The deployment architecture (edge-based intersection controllers vs. centralized cloud orchestration) is not described. Threats include physical cabinet compromise, edge device tampering, and unauthorized remote access to traffic controllers.

Not certain from the listing — Real-time observability and safety guardrails (such as conflict monitors to prevent green-green conflicts) are not detailed. Gaps here could allow anomalous or malicious signal patterns to persist undetected.

Not certain from the listing — Compliance with transportation security standards or critical infrastructure regulations is not mentioned. Unauthorized override capabilities represent a severe risk if robust access controls and audit logging are absent.

Not certain from the listing — It is unclear if intersections operate as isolated agents or coordinate in a multi-agent grid. If networked, a compromise at one intersection could propagate horizontally, causing cascading traffic failures across an entire corridor.