HappyHorse AI Video — agentic threat model

Utilizes text-to-video and image-to-video foundation models. Primary threats include adversarial prompt injection to bypass safety filters, model reprogramming, and generating misaligned, offensive, or copyrighted outputs.

Not certain from the listing — likely processes user-uploaded images and text prompts. Risks include data exfiltration of proprietary user assets, lack of data lineage for training inputs, and potential copyright infringement from training data.

Not certain from the listing — orchestration is likely limited to parsing prompts and managing the video rendering pipeline. Risks include prompt injection manipulating scene control parameters or exploiting vulnerabilities in the underlying generation framework.

Not certain from the listing — requires high-performance GPU infrastructure for video rendering. Threats include denial of service via resource exhaustion (heavy rendering tasks) and container compromise in cloud hosting environments.

Not certain from the listing — requires robust input/output guardrails to detect and block deepfakes, CSAM, or extreme violence. Gaps in observability could lead to undetected generation of harmful content.

Not certain from the listing — needs strong user authentication, access controls for paid tiers, and compliance with emerging AI regulations (e.g., EU AI Act requirements for watermarking synthetic media).

Not certain from the listing — does not explicitly mention multi-agent coordination or marketplace integrations, though users may chain its outputs into broader automated content creation pipelines.