Face Shape Detector — agentic threat model
The Face Shape Detector is a low-risk, single-purpose utility agent focused on image classification and style recommendation. Its primary security risks are concentrated around user privacy (facial image uploads) rather than agentic autonomy or system-level execution capabilities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.40 |
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 a computer vision model or multimodal LLM to analyze facial geometry, which is susceptible to adversarial image perturbations that cause misclassification or model evasion.
Not certain from the listing — processes highly sensitive user-uploaded facial images. Lack of clarity on whether these images are stored, cached, or used for downstream training poses significant data privacy and exfiltration risks.
Not certain from the listing — orchestration appears minimal, likely limited to passing image analysis results to a recommendation template. Risks of tool misuse or complex planning failures are low.
Not certain from the listing — web-hosted deployment must secure the file upload endpoint to prevent arbitrary file execution, path traversal, or denial-of-service via large image payloads.
Not certain from the listing — no visible monitoring for classification drift, demographic bias in face shape detection, or logging of anomalous API usage patterns.
Not certain from the listing — handling biometric-like data (facial images) may subject the application to strict regulations like GDPR or BIPA, yet no privacy policy or compliance framework is detailed.
Not certain from the listing — operates as an isolated, standalone application with no apparent integration into a broader multi-agent ecosystem or external marketplaces.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).