aihappy-horse — agentic threat model

The core of the system is a generative video and audio model. Key threats include adversarial prompt injections to bypass safety filters, model stealing of this proprietary 'top-ranked' model, and the generation of misaligned, harmful, or copyrighted content.

Not certain from the listing — No details are provided regarding training data curation, RAG, or vector stores. General threats include training data poisoning, copyright infringement from scraped video/audio datasets, and potential data exfiltration of user-uploaded images.

Not certain from the listing — There is no evidence of an agentic orchestration framework, planning loops, or tool-calling capabilities. The system operates primarily as a direct inference pipeline, where the main threat is insecure handling of user-provided image/text inputs.

Not certain from the listing — The deployment architecture is web-based but unspecified. Primary threats include server-side resource exhaustion (GPU denial of service) due to heavy video rendering demands, and insecure cloud storage of generated video assets.

Not certain from the listing — No guardrails, evaluation metrics, or observability tools are mentioned. The main threat is a blind spot in detecting the generation of deepfakes, CSAM, or copyrighted material in real-time.

Not certain from the listing — No compliance certifications (e.g., SOC2, GDPR) or identity controls are cited. Threats include lack of robust content moderation policies and potential regulatory non-compliance under emerging AI safety acts regarding synthetic media.

Not certain from the listing — The agent operates standalone in the browser with no ecosystem or multi-agent interactions described. The primary threat would arise if downstream automated agents integrate this service via API without output verification.