Physics AI — agentic threat model
Physics AI is a low-risk educational assistant with minimal agentic capabilities, primarily vulnerable to indirect prompt injection via uploaded images and hallucinated calculations.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.00 |
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.
The agent relies on multimodal foundation models to parse text and images. Key threats include adversarial image inputs (indirect prompt injection via diagrams) and hallucinated or incorrect physics explanations.
Not certain from the listing — the data pipeline for processing uploaded images and storing user queries is unspecified, raising potential data privacy and leakage risks for student uploads.
Not certain from the listing — the orchestration framework is not detailed, but risks are low as there are no active tools, memory, or complex planning capabilities.
Not certain from the listing — hosting and sandboxing details are omitted, though secure image parsing libraries are critical to prevent remote code execution via malicious image uploads.
Not certain from the listing — no mention of guardrails or output monitoring to detect hallucinated calculations or inappropriate content generation.
Not certain from the listing — compliance with student data privacy regulations (e.g., COPPA or FERPA) is unaddressed despite the primary educational use case.
The agent operates as a standalone utility with no multi-agent interactions or marketplace integrations, minimizing ecosystem-level threats.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.