PaymanAI — agentic threat model
PaymanAI presents a high-risk profile due to its direct handling of financial transactions, wallet funding, and automated payments to humans. The primary risks stem from potential prompt injection bypassing task verification, multi-agent trust abuse, and unauthorized financial exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.80 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.50 | |
| 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.
Not certain from the listing — The listing does not specify the underlying LLMs used for task verification or generation. Threats include prompt injection leading to unauthorized task creation or false verification of completed tasks.
Not certain from the listing — No details on data storage, RAG, or vector databases are provided. Threats include exposure of transaction histories, wallet addresses, and user/agent profile data.
The platform orchestrates task creation, wallet funding, and payment processing. Threats include insecure tool integration (payment APIs, wallet APIs) and logic flaws in the automated task verification workflow.
Not certain from the listing — Hosting and sandboxing details are omitted. Threats include compromise of the payment gateway infrastructure, webhook endpoint spoofing, and exposure of API keys/secrets used to access wallets.
The platform uses webhooks and customizable task verification. Threats include blind spots in transaction monitoring, bypass of verification logic by malicious workers, and insufficient logging of anomalous payment patterns.
Handles financial transactions and wallet funding, requiring strict identity, authorization, and compliance (KYC/AML). Threats include unauthorized wallet access, lack of robust multi-factor authorization for high-value transfers, and regulatory non-compliance.
Designed specifically for multi-agent interactions and a marketplace for human workers. Threats include rogue agents draining wallets, collusion between agents and human workers to exploit verification logic, and cascading failures across integrated agent networks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.