Omakase.ai — agentic threat model
Omakase.ai presents a moderate risk profile as a public-facing e-commerce personal shopper. Its zero-code, URL-based ingestion model simplifies deployment but introduces significant risks of indirect prompt injection and data poisoning from scraped storefront content.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.60 | |
| 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 LLM is not specified, but it is susceptible to prompt injection, adversarial manipulation to alter product recommendations, or model reprogramming to output competitor links.
The agent ingests data simply by entering a store's URL. This makes it highly vulnerable to web cache poisoning, indirect prompt injection via on-page content, or scraping of sensitive/unintended pages if the URL ingestion is not properly scoped.
Not certain from the listing — the orchestration framework is undisclosed, but risks include insecure tool integration if the agent attempts to dynamically query live inventory or cart APIs based on user chat inputs.
Not certain from the listing — deployment details are omitted, but as a widget embedded on SMB storefronts, insecure hosting or lack of sandboxing could expose the host site to cross-site scripting (XSS) or data exfiltration.
Not certain from the listing — there is no mention of guardrails or conversation monitoring, creating a risk of undetected drift, hallucinated pricing, or offensive outputs to customers.
Not certain from the listing — compliance frameworks (like PCI-DSS if handling transactions, or GDPR for customer chats) are not detailed, posing compliance risks for SMBs deploying it.
Not certain from the listing — the agent operates as a standalone vertical solution, but future integrations with payment or shipping agents could introduce cascading trust boundaries.
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.