Stemrobo — agentic threat model
Stemrobo presents a unique risk profile combining physical educational hardware with AI-driven learning. The primary concerns center on the privacy of student data (potentially minors) and the secure execution of code on connected physical devices.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.50 |
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 — likely utilizes third-party or proprietary models to generate educational content. Threats include prompt injection leading to inappropriate content generation for children, or model reprogramming.
Not certain from the listing — manages educational content, lesson plans, and student performance data. Threats include data exfiltration of sensitive student PII (potentially minors) and poisoning of the educational knowledge base.
Not certain from the listing — orchestrates coding execution and robot commands. Threats include insecure tool integration where student-submitted code or AI-generated code executes with excessive privileges on the physical robot or local system.
Not certain from the listing — relies on cloud infrastructure to host the platform and local/remote connectivity to interface with physical robots. Threats include compromised communication channels (e.g., BLE/Wi-Fi) between the platform and the hardware, or unauthorized access to classroom networks.
Not certain from the listing — monitoring of student interactions and AI safety guardrails. Threats include a lack of robust content moderation and output filtering, allowing harmful or biased content to reach young learners.
Not certain from the listing — compliance with child privacy regulations (like COPPA or GDPR-K) is critical but unverified. Threats include regulatory non-compliance and weak authentication mechanisms for classroom accounts.
Not certain from the listing — potential ecosystem interactions between the platform, physical robots, and third-party educational tools. Threats include unauthorized third-party integrations or compromised educational packages.
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.