Hotel Lobby AI Video — agentic threat model
The Hotel Lobby AI Video agent is a low-risk, template-driven media generation tool with no tool-use, planning, or persistent memory capabilities. Its primary security risks are limited to input manipulation (adversarial images) and the potential generation of unauthorized deepfakes or inappropriate content.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.70 | |
| 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.
The agent relies on generative video and audio foundation models to animate reference photos. Threats include adversarial image inputs designed to bypass safety filters, model reprogramming, or exploiting vulnerabilities in the underlying diffusion/rendering models.
Not certain from the listing — The data pipeline processes user-uploaded reference photos. Potential threats include data exfiltration of these images, lack of secure transient storage, and privacy violations if uploaded faces are cached or used for downstream training without consent.
Not certain from the listing — There is no complex agentic orchestration framework described; the system uses a rigid template-driven pipeline. Consequently, typical agentic threats like tool misuse, prompt injection-based hijacking, or memory poisoning are not applicable.
Not certain from the listing — The infrastructure must host heavy GPU-based video generation workloads. Threats include resource exhaustion (DoS) due to high computational demands, and container compromise via vulnerabilities in image/video processing libraries.
Not certain from the listing — The description mentions 'non-deterministic visual output for review,' implying a human-in-the-loop review of the final video. However, automated guardrails to detect copyright infringement, NSFW content, or deepfake policy violations are not specified.
Not certain from the listing — There is no mention of identity management, access controls, or compliance frameworks. The primary compliance risk is the unauthorized generation of likenesses (biometric/privacy concerns) without the consent of the subjects in the uploaded photos.
The agent operates as a standalone productivity tool with no multi-agent interactions or marketplace integrations described, resulting in minimal ecosystem risk.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.