Retell AI — agentic threat model
Retell AI presents a moderate-to-high risk profile primarily centered on real-time conversational manipulation, voice-based social engineering (vishing), and prompt injection vulnerabilities within its low-latency LLM-integrated voice API.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.10 | |
| 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.
Integrates with external LLMs to drive conversational voice. Vulnerable to prompt injection via voice input (indirect injection), which could manipulate the agent's behavior, bypass safety alignment, or generate malicious/harmful spoken outputs.
Not certain from the listing — No details are provided regarding data storage, RAG, or custom voice training data. General threats include the unauthorized exfiltration or poisoning of voice transcripts and custom voice synthesis templates.
Manages conversational state, interruption handling, and end-of-turn detection. Vulnerabilities in this orchestration layer could allow attackers to disrupt conversational flow, inject commands during interruption windows, or exploit state-handling logic.
Not certain from the listing — No details are provided regarding hosting, API security, or sandboxing. General threats include API key theft, unauthorized access to low-latency WebRTC/WebSocket streams, and denial-of-service attacks on the voice processing infrastructure.
Not certain from the listing — No mention of built-in guardrails, real-time voice monitoring, or safety filters. General threats include the lack of automated detection for toxic, abusive, or socially engineered voice outputs generated by the LLM.
Not certain from the listing — No compliance certifications (such as SOC2, HIPAA, or GDPR) are specified. General threats involve the handling and potential exposure of sensitive audio recordings and personally identifiable information (PII) transmitted over voice.
Not certain from the listing — No multi-agent ecosystem or marketplace is described. General threats involve downstream applications blindly trusting the voice agent's output, leading to secondary exploitation if the voice agent is compromised.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).