aiMotive — agentic threat model
aiMotive represents an exceptionally high-risk agentic profile due to its direct control over physical actuators in autonomous driving (aiDrive), where cyber-physical compromise can result in severe real-world safety failures and physical harm.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 1.00 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.50 | |
| 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 — The specific neural network architectures or foundation models powering aiDrive are not detailed. Key threats include physical adversarial attacks (e.g., adversarial stickers on road signs) and model extraction/stealing of proprietary automotive weights.
Not certain from the listing — The data pipelines for training aiDrive are proprietary. Threats include training data poisoning (e.g., corrupting perception datasets) and simulation data leakage from aiSim.
Not certain from the listing — The internal orchestration and decision-making logic of the aiDrive software stack are not disclosed. Threats include control-loop hijacking, sensor fusion manipulation, and unsafe path-planning overrides.
Not certain from the listing — While aiWare provides hardware IP for AI acceleration, the exact on-vehicle deployment architecture is unspecified. Threats include hardware-level side-channel attacks, physical tampering, and firmware-level privilege escalation.
The listing explicitly highlights 'aiSim' as a virtual simulation environment for testing. The primary threat is the Sim-to-Real gap, where models perform safely in simulation but fail under real-world edge cases, alongside insufficient real-time anomaly detection in physical deployments.
Not certain from the listing — No specific automotive cybersecurity standards (such as ISO/SAE 21434 or ISO 26262) are explicitly cited in the directory text. Threats include non-compliance with regional autonomous vehicle safety regulations and lack of verifiable audit trails.
Not certain from the listing — The extent of V2X (Vehicle-to-Everything) or fleet-wide multi-agent coordination is not detailed. Threats include cascading failures if compromised vehicles propagate malicious traffic or sensor data to surrounding infrastructure.
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.