Figure AI — agentic threat model
Figure AI represents a high-risk agentic profile due to its physical embodiment and deployment in industrial environments, where planning failures or adversarial manipulation can translate directly into physical safety hazards and operational disruption.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.90 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.70 | |
| 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 — Figure AI likely utilizes proprietary vision-language-action (VLA) foundation models to map sensory inputs to physical actions. Threats include adversarial physical inputs (e.g., visual patches) causing misaligned or dangerous physical outputs.
Not certain from the listing — Data operations involve massive physical trajectory datasets, simulation-to-real (Sim2Real) training data, and real-world telemetry. Threats include training data poisoning or simulation transfer gaps leading to unpredictable physical behaviors.
Not certain from the listing — The agent framework orchestrates high-level task planning down to low-level motor control. Threats include planning failures, logic loops, or insecure tool integration where the robot misuses physical machinery or tools.
Not certain from the listing — Deployment is on physical humanoid hardware (edge compute) with cloud connectivity for telemetry and updates. Threats include physical tampering, OTA update compromise, and privilege escalation on the robot's onboard operating system.
Not certain from the listing — Requires real-time physical safety guardrails, collision avoidance, and telemetry logging. Threats include sensor spoofing (e.g., LiDAR/camera blinding) bypassing physical safety limits or insufficient logging of anomalous physical behaviors.
Not certain from the listing — Compliance must cover physical safety standards (e.g., ISO 10218, ISO/TS 15066 for collaborative robots) and enterprise access controls. Threats include unauthorized remote control, lack of physical emergency stop overrides, or regulatory non-compliance in industrial settings.
Not certain from the listing — Fleet operations imply multi-agent coordination in warehouses. Threats include cascading failures across a fleet, rogue robot commands propagating through the local network, or unauthorized fleet-wide policy updates.
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.