Submind — agentic threat model

Not certain from the listing — The specific local or remote models used for transcription and chat are not disclosed. Potential threats include adversarial prompt injection when chatting with untrusted PDFs/notes, or local model manipulation if an attacker gains device access.

Submind stores notes, recordings, and media locally on the device. The primary threat is unauthorized local access to the database/vector store, or data poisoning if a user imports a maliciously crafted PDF designed to hijack the local RAG system.

Not certain from the listing — The orchestration framework is not detailed. Threats include insecure handling of parsed PDF/audio content during local RAG/chat operations, which could lead to local prompt injection or application crashes.

The application runs locally on-device (offline-first). Threats include local privilege escalation, insecure local file permissions allowing other malicious applications on the same device to read the database, and lack of sandboxing on the host OS.

Not certain from the listing — There is no mention of local logging, guardrails, or evaluation metrics. Threats include silent failures in transcription or RAG summarization without user visibility.

The app emphasizes privacy by keeping data on-device and avoiding external servers. However, compliance risks exist if local storage is not encrypted (e.g., lack of compliance with GDPR/HIPAA for local medical/personal notes if the device is lost or stolen).

There is no multi-agent or marketplace interaction described. The threat of rogue agent interactions or cascading ecosystem failures is virtually non-existent here.