Echo — agentic threat model
Echo (Echovane) presents a moderate security risk primarily centered on the exposure of sensitive qualitative research data, user PII, and voice recordings. Its autonomous conversational capabilities require robust guardrails to prevent prompt injection during live interviews and to ensure data privacy compliance.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| 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 — The underlying foundation models for voice synthesis, transcription, and text generation are unspecified. Threats include adversarial prompt injection during live voice interviews to manipulate the agent's behavior or extract system instructions.
Not certain from the listing — The storage mechanism for interview transcripts, audio recordings, and panel recruitment data is not detailed. Risks include data exfiltration of sensitive qualitative research and PII, as well as unauthorized access to the knowledge base used for generating survey designs.
Not certain from the listing — The orchestration framework managing the transition between survey design, conversational interviewing, and analysis is proprietary. Vulnerabilities could lead to insecure tool calling (e.g., transcription or coding tools) or state manipulation across interview sessions.
Not certain from the listing — No details are provided regarding hosting, sandboxing of analysis scripts, or secrets management. Compromise of the hosting infrastructure could expose voice processing pipelines and database credentials.
Not certain from the listing — There is no mention of real-time guardrails, logging, or drift detection for the conversational voice AI. This creates blind spots where biased probing or inappropriate agent behavior during interviews could go undetected.
Not certain from the listing — Compliance with data protection regulations (like GDPR or CCPA) is not explicitly stated, which is critical given the collection of voice biometrics and personal opinions during user research.
Not certain from the listing — While the description implies multiple specialized agents (design, interviewing, analysis), the interaction protocols between them are closed. Risks include cascading failures or trust abuse if one agent in the pipeline is compromised.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).