Aurora Innovation — agentic threat model

Not certain from the listing — The specific deep learning models and foundation architectures used for perception and decision-making are proprietary, but they are highly vulnerable to physical adversarial attacks (e.g., adversarial stickers on road signs) and model evasion techniques.

Not certain from the listing — The data pipelines for continuous learning, mapping, and sensor fusion are not detailed, presenting risks of training data poisoning, sensor spoofing, and localization data corruption.

Not certain from the listing — The proprietary orchestration and planning framework of the Aurora Driver is not described, but threats include planning logic bypasses, unsafe tool/actuator command execution, and failure to handle edge-case scenarios safely.

Not certain from the listing — The on-vehicle hardware, real-time operating systems, and over-the-air (OTA) update mechanisms are not detailed, leaving potential vulnerabilities to physical tampering, CAN bus exploitation, and firmware interception.

Not certain from the listing — Specific simulation, testing, and real-time safety-driver override monitoring systems are not detailed, creating risks of simulation-to-reality gaps and silent failures of safety-critical monitors.

Not certain from the listing — While safety is a stated goal, the listing does not specify compliance with automotive cybersecurity standards such as ISO/SAE 21434 or ISO 26262, leaving potential regulatory and audit gaps.

Not certain from the listing — Fleet-wide coordination and Vehicle-to-Everything (V2X) communication ecosystems are not detailed, but threats include rogue vehicle-to-vehicle communications and cascading fleet-wide routing failures.