Kaomojiya — agentic threat model
Kaomojiya is a static web application for copying text emoticons rather than an active AI agent, presenting negligible agentic risk. Its primary security considerations are standard web application vulnerabilities like client-side scripting or dependency supply chain issues.
OWASP AIVSS score rationale
| Autonomy of Action | 0.00 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.00 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.00 | |
| Opacity & Reflexivity | 0.00 |
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 application does not appear to utilize a foundation model, as it is a static directory of curated Unicode kaomoji. Consequently, LLM-specific threats like adversarial prompt injection or model stealing are not applicable.
Not certain from the listing — There is no active RAG or vector database; the data consists of a static list of 5,000+ curated kaomoji. Risks of data poisoning are limited to source code repository compromise (GitHub) rather than dynamic database injection.
Not certain from the listing — The application is built on Next.js 14 and TypeScript rather than an AI agent framework (like LangChain or AutoGPT). There are no tools, planning loops, or memory structures to exploit.
The application is deployed on Cloudflare Pages, leveraging global edge delivery. Infrastructure risks are minimized due to the serverless, static nature of the deployment, though standard web risks like domain hijacking or CDN configuration errors remain.
Not certain from the listing — No AI-specific evaluation, guardrails, or LLM observability tools are mentioned or required, as there is no generative AI output to monitor.
The application requires no registration, downloads, or user accounts, eliminating user data privacy concerns (GDPR/CCPA). Compliance risks are extremely low, focusing primarily on open-source license compliance.
Not certain from the listing — The application operates as a standalone web utility with no multi-agent interactions, marketplace integrations, or agent-to-agent trust boundaries.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).