AI VIDEO SPLITTER — agentic threat model
The AI Video Splitter is a local-first, deterministic utility tool with virtually zero agentic risk, as it lacks autonomous decision-making, LLM integration, or server-side data persistence.
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 tool is branded as an 'AI Toolbox' but describes purely deterministic WebAssembly video splitting with no explicit foundation model usage.
Not certain from the listing — No training, RAG, or vector stores are mentioned; data operations are strictly limited to local browser-based video file processing.
Not certain from the listing — There is no evidence of an agent framework, planning, memory, or tool orchestration; it operates as a standard client-side utility.
Runs entirely client-side in the user's browser via WebAssembly. The primary infrastructure threat is front-end supply chain compromise (e.g., malicious JS injection on the hosting CDN) rather than server-side container escape.
Not certain from the listing — No monitoring, logging, or guardrails are mentioned, which is typical for a local-first, privacy-focused client-side tool.
Compliance risk is extremely low due to the 'Zero-Upload Privacy' model where files never leave the local device, eliminating server-side data exposure and regulatory compliance overhead.
No multi-agent interactions, marketplace integrations, or external agent dependencies exist; it is a standalone horizontal tool.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).