Amical — agentic threat model
Amical is a low-risk, utility-focused voice transcription agent with minimal agentic autonomy, whose primary security boundary lies in protecting the confidentiality of transcribed audio and local notes.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.20 |
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.
Uses local or cloud-based speech-to-text and language models. Vulnerable to adversarial audio injection (e.g., hidden voice commands in audio) or model manipulation if cloud APIs are compromised.
Processes sensitive voice recordings, transcriptions, and custom vocabulary. Risks include unauthorized local access to stored transcripts or poisoning of the custom vocabulary database to mis-transcribe specific terms.
Not certain from the listing — The agent does not appear to use a complex agentic orchestration framework, operating instead as a direct pipeline from audio input to transcription output.
Supports both local and cloud deployment. Local deployment mitigates cloud-based data exposure but shifts the security burden to the user's local host environment, where malicious local binaries could access the microphone or transcript files.
Not certain from the listing — No built-in evaluation, monitoring, or transcription guardrails are mentioned to detect drift or malicious inputs.
Not certain from the listing — While local execution provides inherent privacy benefits, there is no explicit mention of access controls, encryption of stored notes, or compliance certifications.
Operates as a standalone vertical application with no multi-agent coordination or ecosystem marketplace integrations described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).