  This paper examines the dual-use challenges of foundation models and the
consequent risks they pose for international security. As artificial
intelligence (AI) models are increasingly tested and deployed across both
civilian and military sectors, distinguishing between these uses becomes more
complex, potentially leading to misunderstandings and unintended escalations
among states. The broad capabilities of foundation models lower the cost of
repurposing civilian models for military uses, making it difficult to discern
another state's intentions behind developing and deploying these models. As
military capabilities are increasingly augmented by AI, this discernment is
crucial in evaluating the extent to which a state poses a military threat.
Consequently, the ability to distinguish between military and civilian
applications of these models is key to averting potential military escalations.
The paper analyzes this issue through four critical factors in the development
cycle of foundation models: model inputs, capabilities, system use cases, and
system deployment. This framework helps elucidate the points at which ambiguity
between civilian and military applications may arise, leading to potential
misperceptions. Using the Intermediate-Range Nuclear Forces (INF) Treaty as a
case study, this paper proposes several strategies to mitigate the associated
risks. These include establishing red lines for military competition, enhancing
information-sharing protocols, employing foundation models to promote
international transparency, and imposing constraints on specific weapon
platforms. By managing dual-use risks effectively, these strategies aim to
minimize potential escalations and address the trade-offs accompanying
increasingly general AI models.
