Motional — agentic threat model
Motional represents an extreme-risk profile due to its physical actuation capabilities as an SAE Level 4 autonomous vehicle, where cyber-physical compromise directly threatens human life and public safety.
OWASP AIVSS score rationale
| Autonomy of Action | 0.95 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 1.00 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.70 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.80 |
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 — likely utilizes specialized deep learning models for perception, localization, and path planning rather than standard LLMs. Key threats include adversarial physical attacks (e.g., perturbed road signs) and sensor-level evasion.
Not certain from the listing — relies on massive pipelines for HD mapping, sensor logs, and training data. Key threats include poisoning of HD map data or training sets, which could cause systemic navigation failures.
Not certain from the listing — orchestrates actions via proprietary automotive middleware and real-time operating systems. Threats include logic flaws in the planning/routing engine and unauthorized tool (actuator) execution.
Not certain from the listing — deployed on in-vehicle edge compute hardware with cellular connectivity. Threats include physical access exploitation, cellular baseband compromise, and insecure Over-The-Air (OTA) firmware updates.
Motional utilizes a rigorous multi-stage testing process and processes inputs from over 30 sensors. Threats include sensor drift, blind spots, and failure of real-time anomaly detection systems to safely trigger fallback states.
Not certain from the listing — must comply with automotive safety and cybersecurity standards (such as ISO 26262 and ISO 21434) and regional SAE Level 4 regulations, though specific compliance frameworks are not detailed.
Integrates with ride-hailing networks (specifically Lyft) and fleet management systems. Threats include API compromise at the ride-hailing integration layer, leading to unauthorized vehicle dispatching, tracking, or interception.
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.