Sigma AI Browser — agentic threat model

Uses 'Sigma Eclipse', a cloudless local LLM architecture. Because the model runs locally on the user's device, it is highly susceptible to model extraction, reverse-engineering, and local adversarial reprogramming.

Not certain from the listing — details on local vector databases or RAG implementations are omitted. However, the agent's ability to summarize articles implies it processes untrusted web data, risking indirect prompt injection and local data poisoning from malicious websites.

Not certain from the listing — the orchestration framework for the 'full AI agent' is unspecified. The primary threat is insecure tool integration, where the agent framework might execute browser-level actions or access sensitive APIs based on untrusted web inputs.

Deployed locally on the user's device with offline capabilities. This eliminates cloud-hosting vulnerabilities but introduces risks of local host compromise, where other malicious local processes could access the browser's memory space or LLM weights.

Not certain from the listing — there is no mention of local guardrails, logging, or observability tools. The lack of centralized monitoring makes detecting local model drift or silent failures difficult.

Not certain from the listing — while the description claims to transform the online space into a 'safe' place where 'privacy' comes together, there are no references to formal security audits, compliance certifications, or local data encryption standards.

Not certain from the listing — there is no evidence of a multi-agent ecosystem or marketplace. The threat of rogue agent-to-agent interactions is currently low, unless third-party browser extensions are allowed to interface with the local agent.