Agents.ai — agentic threat model

Uses closed-source Large Action Models (LAMs). The 'Browse-2-Earn' program introduces a significant threat of training data poisoning, as malicious users could manipulate their browsing data to corrupt the model's downstream action logic.

Data operations involve collecting and processing user browsing data. This creates severe privacy risks, potential data exfiltration of sensitive session data, and challenges in maintaining data lineage and consent boundaries.

The agent framework operates via a browser extension to orchestrate multi-step actions. This introduces threats of tool misuse and insecure execution, as the agent can interact with any web element, potentially leading to unauthorized transactions or form submissions.

Deployed as a client-side browser extension. Threats include local storage compromise, session hijacking, and privilege escalation within the browser environment, granting the agent access to authenticated user sessions.

Not certain from the listing — there is no mention of real-time monitoring, guardrails, or audit logging to detect and prevent anomalous or malicious actions taken by the browser extension.

Not certain from the listing — while the extension is described as 'secure', there are no details on compliance frameworks, authorization policies, or mechanisms to prevent the abuse of user credentials during automated tasks.

Not certain from the listing — the description focuses on single-agent browser automation and does not detail multi-agent coordination or marketplace interactions.