TinyFat — agentic threat model
TinyFat is a highly agentic deployment and orchestration platform that introduces significant risk due to its autonomous execution capabilities, persistent memory, and deep integration with external APIs without explicit built-in guardrails or sandboxing details.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.60 | |
| 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 — as an orchestration platform, TinyFat likely supports multiple third-party foundation models, making it susceptible to model-agnostic threats like prompt injection, adversarial reprogramming, and misaligned outputs depending on the chosen model.
Not certain from the listing — while the platform provides memory and state management, the specific storage mechanisms (vector DBs, relational DBs) are not detailed, exposing potential risks of memory poisoning or data exfiltration if state storage is not isolated.
As an orchestration platform for multi-step workflows and tool integration, L3 is highly critical. Threats include insecure tool integration, tool misuse, and framework vulnerabilities allowing arbitrary code execution via compromised agent definitions.
The platform hosts and deploys agents on its scalable infrastructure. Key threats include container escape, privilege escalation, lateral movement between tenant agents, and insecure secrets management for external APIs.
Not certain from the listing — there is no explicit mention of built-in evaluation, logging, guardrails, or observability tools, which could lead to blind spots in detecting anomalous agent behavior or drift.
Not certain from the listing — no specific compliance certifications (e.g., SOC2, ISO) or identity/access management controls are detailed for the deployment platform.
By enabling orchestration of complex workflows across various digital environments, the platform faces ecosystem risks such as cascading failures, trust abuse between orchestrated agents, and unauthorized external API interactions.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).