CallZero AI — agentic threat model

Not certain from the listing — The underlying LLM and Text-to-Speech/Speech-to-Text models are proprietary and undisclosed, leaving them vulnerable to prompt injection via voice (VPI) or adversarial audio manipulation by the call recipient.

Not certain from the listing — The agent must ingest highly sensitive user data (billing details, account numbers, personal identifiers) to conduct calls, but the storage, encryption, and RAG architecture of this data are unspecified.

The agent framework orchestrates multi-step phone calls and negotiations. A key threat is tool misuse or logic exploitation where the agent is manipulated by the call recipient into agreeing to unfavorable terms, unauthorized cancellations, or leaking user data.

Not certain from the listing — Telephony infrastructure (SIP trunks, VoIP gateways) and hosting environments are undisclosed, presenting risks of call interception, caller ID spoofing abuse, or infrastructure compromise.

Not certain from the listing — While the agent provides a post-call summary, it is unclear if there is real-time monitoring, transcript logging, or guardrails to detect and terminate calls if the AI begins behaving maliciously or erratically.

Not certain from the listing — Handling financial negotiations and subscription cancellations requires strict identity verification and authorization controls to prevent unauthorized third-party actions, which are not detailed in the listing.

Not certain from the listing — The agent primarily interacts with human customer service representatives or automated IVR systems, but could face cascading failures if interacting with other automated voice agents or AI-driven call centers.