Moemate — agentic threat model

Moemate utilizes advanced foundation models for multilingual text, image upload, screen perception, and voice cloning. The primary threats include adversarial prompt injection via screen inputs or uploaded images, which could reprogram the model's behavior or cause misaligned/harmful outputs.

The platform leverages long-term memory and processes sensitive user data (screen captures, voice samples, and images). Threats include memory poisoning (injecting malicious instructions into long-term memory to persist across sessions) and unauthorized exfiltration of captured screen data.

With customizable AI agent skills and modules, the orchestration framework is vulnerable to insecure tool integration and malicious custom modules that could execute unauthorized actions or abuse the agent's autonomous capabilities.

Not certain from the listing — how Moemate sandboxes its screen perception tools, executes custom modules, or secures its hosting infrastructure is unspecified, leaving potential gaps for container escape or privilege escalation.

Not certain from the listing — there is no mention of real-time guardrails, output filtering, or logging mechanisms to detect and prevent the generation of abusive content, deepfakes, or malicious interactions by custom agents.

Not certain from the listing — compliance with biometric data privacy laws (regarding voice cloning) and user data protection policies is not detailed, posing potential regulatory and identity-theft risks.

Moemate features a sharing ecosystem where users can distribute custom characters and modules. This creates a significant marketplace threat where compromised or malicious agents can be shared, leading to widespread user exploitation or cascading trust failures.