Happy Horse — agentic threat model

Uses advanced video diffusion and motion generation models. Primary threats include adversarial prompt injections to bypass safety filters (generating NSFW or deepfakes), model extraction/stealing, and training data poisoning affecting motion quality.

Not certain from the listing — No details are provided regarding training data pipelines, vector stores, or RAG. General threats include copyright infringement in training datasets and potential leakage of user-uploaded seed images or video assets.

Not certain from the listing — The tool appears to function as a direct generative pipeline rather than a complex agentic framework. If orchestration code exists, threats are limited to insecure handling of generation parameters and API prompt injection.

Not certain from the listing — No hosting or infrastructure details are provided. Because video generation is highly GPU-intensive, key threats include GPU resource exhaustion (DoS), container escape, and unauthorized access to model weights.

Not certain from the listing — While ranked on the Artificial Analysis leaderboard, there is no mention of runtime guardrails or observability. The main threat is a lack of automated detection for abusive, copyrighted, or harmful video generations.

Not certain from the listing — No compliance certifications, access controls, or content moderation policies are detailed. The absence of digital watermarking or content provenance (e.g., C2PA) poses compliance and reputational risks.

Not certain from the listing — No multi-agent or marketplace interactions are described. Downstream risks are limited to integration into creative workflows where malicious inputs could trigger harmful video outputs.