Simbo AI — agentic threat model

Not certain from the listing — likely utilizes proprietary or fine-tuned LLMs optimized for low-latency voice synthesis and speech-to-text. Primary threats include voice prompt injection (VPI), adversarial audio inputs, and potential generation of inaccurate or harmful medical advice.

Not certain from the listing — must interface with Electronic Health Records (EHR) and scheduling databases to execute 50+ patient call functions. Key threats include unauthorized PHI exfiltration, database poisoning via malicious patient inputs, and lack of data lineage for voice-derived records.

Not certain from the listing — orchestrates complex multi-step call flows (appointments, follow-ups). Threats include insecure tool calling (e.g., booking appointments without proper authentication) and state-machine manipulation by malicious callers.

Not certain from the listing — requires integration with telephony infrastructure (SIP/VoIP) and low-latency hosting. Threats include SIP flooding, toll fraud, eavesdropping on voice streams, and container escape from the voice processing environment.

Not certain from the listing — mentions eight patents ensuring 'responsible AI and safety', suggesting proprietary guardrails. However, threats remain regarding blind spots in real-time voice monitoring and the difficulty of auditing non-deterministic spoken interactions.

Not certain from the listing — operating in healthcare necessitates strict HIPAA compliance and robust audit trails, but specific certifications are not explicitly listed. Threats include regulatory non-compliance and lack of explicit patient consent logging during automated calls.

Not certain from the listing — primarily operates as a standalone copilot interacting with human patients and internal systems, rather than a multi-agent marketplace. Threats are limited to downstream API vulnerabilities in connected healthcare ecosystems.