Simli — agentic threat model

Not certain from the listing — Simli likely relies on third-party or proprietary LLMs for text generation paired with specialized video synthesis models, exposing the system to prompt injection, model evasion, and adversarial manipulation of avatar behavior.

Not certain from the listing — The platform processes real-time voice and video data, which introduces risks of data exfiltration, unauthorized logging of sensitive conversations, and potential privacy violations if user inputs are used for model fine-tuning.

Not certain from the listing — The orchestration framework must synchronize LLM text outputs with real-time video rendering pipelines; vulnerabilities here could allow attackers to bypass conversational constraints or inject malicious instructions into the rendering engine.

Not certain from the listing — Low-latency streaming requires high-performance GPU infrastructure and WebRTC/WebSocket connections, making the deployment layer highly susceptible to DDoS, resource exhaustion, and API endpoint exploitation.

Not certain from the listing — Real-time guardrails and observability tools are critical to prevent avatars from generating toxic, brand-damaging, or socially engineered outputs during unscripted interactions, but specific monitoring capabilities are not detailed.

Not certain from the listing — As an API platform, robust authentication, rate limiting, and compliance with biometric/voice data privacy regulations (GDPR/CCPA) are necessary but not explicitly detailed in the public directory.

Not certain from the listing — While designed for integration into third-party applications, there is no mention of a multi-agent marketplace or direct agent-to-agent trust boundaries within the platform itself.