Kling AI Video Generator — agentic threat model

Utilizes advanced video and audio generation foundation models. Primary threats include model stealing (IP theft of the proprietary Kling model), adversarial prompt injection to bypass safety filters, and the generation of mis-aligned or harmful outputs (e.g., deepfakes, copyright violations).

Not certain from the listing — requires massive video, image, and audio datasets for training and inference. Key threats include training data poisoning, copyright/licensing provenance gaps, and potential data exfiltration of user-uploaded source images.

Not certain from the listing — likely uses a standard web backend to orchestrate model inference rather than a complex agentic framework. Threats are limited to insecure orchestration of generation pipelines and parameter manipulation.

Not certain from the listing — operates as a browser-based service likely backed by high-performance GPU cloud infrastructure. Threats include GPU resource exhaustion (DoS), container escape, and unauthorized access to model weights in storage.

Not certain from the listing — likely relies on input/output content moderation guardrails to prevent NSFW or harmful generations. Threats include blind spots in video-based safety filters and evaluation gaming by malicious users.

Not certain from the listing — requires standard web authentication. Key compliance threats involve alignment with the EU AI Act (specifically regarding deepfake labeling/watermarking) and user data privacy (GDPR) for uploaded media.

The agent operates as a standalone horizontal tool with no multi-agent coordination or marketplace ecosystem described, making ecosystem-level cascading failures or A2A trust abuse highly unlikely.