OpenCV AI Kit (OAK) — agentic threat model
The OpenCV AI Kit (OAK) presents a physical and spatial security risk profile due to its deployment on edge hardware and robotics. While it lacks high-level LLM planning capabilities, vulnerabilities in its vision models (adversarial attacks) or firmware could lead to physical safety hazards or network compromise via PoE.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.60 |
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.
Runs edge-based neural networks for classification, detection, and segmentation. Key threats include physical adversarial examples (e.g., adversarial patches bypassing object detection) and model extraction/stealing directly from the physical device's memory.
Processes real-time camera feeds and depth data. Threats include data poisoning of custom training sets used for edge deployment, and potential privacy violations or data exfiltration if video streams are intercepted.
Not certain from the listing — The ecosystem uses DepthAI API/software rather than a traditional LLM agent framework. Threats involve insecure tool integration where vision outputs trigger unsafe physical actions in downstream robotics controllers.
Deployed on physical hardware (OAK-1, OAK-D) with USB or PoE connectivity. Threats include physical tampering, firmware modification, side-channel attacks on the Myriad X VPU, and network-based exploitation of PoE-connected devices.
Not certain from the listing — There is no mention of built-in model drift detection or real-time guardrails. Gaps in observability could allow silent failures in object detection to go unnoticed in production environments.
Not certain from the listing — No compliance certifications (e.g., ISO, NIST) or built-in access control/authentication mechanisms are specified for the hardware or firmware.
Not certain from the listing — While OAK devices can be integrated into multi-robot systems, there is no native multi-agent ecosystem or marketplace described. Risks are limited to cascading failures in custom multi-device deployments.
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.