When AI Agents Form Societies: Is Security a Property of the Model or the System?

Abstract

Evaluating a language model on a bounded prompt tells us something about its responses under those conditions. It tells us less about a persistent group of agents that remembers previous interactions, uses tools, exchanges messages, and changes a shared environment. Two Emergence World studies provide a useful setting for examining that gap: the first compares societies initialized under similar conditions; the second introduces controlled adversarial events after the societies have accumulated history [1, 2]. This article explains their security implications, distinguishes observed results from broader interpretation, and asks which properties need evaluation at model and system levels. It does not present a new SGAEIA experiment or a validated SGAEIA implementation.