PayOS — agentic threat model

Not certain from the listing — The underlying foundation model is unspecified. Adversarial prompt injection could potentially bypass spending limits or redirect payments to unauthorized merchants.

Not certain from the listing — No details are provided regarding data storage, RAG, or vector databases. Risks include the exposure of transaction histories or cached checkout details.

The agent framework orchestrates checkout navigation and payment execution. Vulnerabilities here include tool misuse where the agent is manipulated into purchasing unintended items or interacting with malicious checkout flows.

Not certain from the listing — The hosting infrastructure and sandboxing mechanisms are not described. A compromise at this layer could expose payment credentials or API keys used to generate virtual cards.

Not certain from the listing — There is no mention of transaction logging, real-time anomaly detection, or human-in-the-loop verification mechanisms for high-value purchases.

The service implements security controls via customizable spending limits and merchant restrictions, and handles transaction security to avoid direct merchant PCI compliance requirements. However, policy enforcement bypass remains a critical threat.

Not certain from the listing — While designed to empower other AI agents with payment capabilities, the specific multi-agent trust boundaries and delegation protocols are not detailed. Upstream compromised agents could abuse the payment delegation.