Navya Autonomous Vehicles — agentic threat model
Navya (Gama) represents a high-risk physical agentic system where compromise of its Level 4 autonomous driving stack could lead to severe real-world safety hazards, including collisions and physical injury.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.90 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.70 |
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 foundation models or neural networks used for perception and path planning are not detailed. Potential threats include adversarial physical attacks (e.g., adversarial patches on road signs) and sensor spoofing that misleads the model.
Not certain from the listing — The training data pipelines, map data ingestion, and vector stores are not described. Threats include training data poisoning for perception models and lack of data lineage for critical navigation maps.
Not certain from the listing — The orchestration framework (such as ROS or proprietary AD stacks) is not specified. Threats include tool misuse (unauthorized actuator commands) and planning failures under edge-case scenarios.
Not certain from the listing — The vehicle's onboard compute architecture, CAN bus security, and cloud connectivity are not detailed. Threats include physical or remote compromise of the vehicle's ECU, CAN bus injection, and lack of sandboxing between critical driving systems and infotainment.
Not certain from the listing — The monitoring, logging, and safety driver override mechanisms are not detailed. Threats include blind spots in anomaly detection and insufficient logging of safety-critical disengagements.
Not certain from the listing — Specific cybersecurity compliance frameworks (such as ISO/SAE 21434 or ISO 26262 for functional safety) are not explicitly mentioned, though Level 4 autonomy implies strict safety standards.
Not certain from the listing — Multi-agent coordination (V2X, fleet management) is not detailed. Threats include rogue/compromised fleet management agents and cascading failures in V2X communication.
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.