Digits — agentic threat model
Digits presents a high-risk profile due to its autonomous financial capabilities, including bill pay and invoicing, where compromise could lead to direct financial theft or fraudulent ledger manipulation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.60 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.50 |
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 underlying foundation models are not specified. Risks include prompt injection leading to unauthorized financial actions or model reprogramming to misclassify transactions.
Handles highly sensitive financial data, bank feeds, and general ledger entries. Risks include data exfiltration of proprietary business financials and poisoning of transaction data to skew automated bookkeeping.
Orchestrates complex workflows like bill pay and invoicing. Insecure tool integration or prompt injection could allow an attacker to trigger unauthorized payments or generate fraudulent invoices.
Not certain from the listing — The hosting environment and sandboxing mechanisms for executing financial workflows are undisclosed. Risks include container compromise or lateral movement to banking API integrations.
Features 'human oversight only where needed' which acts as a partial guardrail, but autonomous execution of bill pay and bookkeeping creates significant blind spots if anomalous transactions are not flagged.
Not certain from the listing — While handling financial data implies strict compliance needs (e.g., SOC2, GLBA), specific identity, authorization, and audit controls are not detailed in the public directory.
Utilizes multiple specialized AI agents to handle distinct workflows (bookkeeping, invoicing, reporting). Risks include cascading failures or trust abuse between the invoicing agent and the bill pay agent.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).