coachcall.ai — agentic threat model
coachcall.ai presents a moderate security risk primarily due to its direct access to communication channels (voice calls and WhatsApp) and its retention of sensitive personal data. A compromise could enable highly convincing social engineering, vishing, or privacy breaches.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| 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.
Not certain from the listing — likely relies on commercial LLMs combined with text-to-speech and speech-to-text models. Key threats include prompt injection via voice or WhatsApp that could hijack the agent's persona or cause it to generate inappropriate outputs.
Not certain from the listing — stores user goals, conversation history, and progress over time. Threats include unauthorized access to this sensitive personal data, lack of encryption at rest, and potential data poisoning of the long-term memory store.
Not certain from the listing — orchestrates scheduling, voice calls, and WhatsApp messaging. Threats include insecure tool integration with telephony/messaging APIs and memory poisoning where malicious user inputs alter the agent's long-term behavior.
Not certain from the listing — requires integration with telephony providers (e.g., Twilio) and WhatsApp Business API. Threats include API key exposure, insecure webhook endpoints, and lack of sandboxing for user-specific session data.
Not certain from the listing — no mention of monitoring or guardrails for voice/text outputs. Threats include blind spots in detecting inappropriate or harmful advice generated during voice calls.
Not certain from the listing — handles highly personal data and voice/phone communications without explicit mention of privacy compliance (e.g., GDPR, CCPA) or robust authentication.
Not certain from the listing — operates primarily as a standalone B2C agent. Threats of multi-agent cascading failures are low, but integration with external calendars/scheduling tools presents minor ecosystem risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).