AIs with Secret Loyalties are a Serious but Addressable Threat

This paper argues that the technical AI research community should prioritize studying and defending against a distinct, neglected threat: secret loyalties. A secretly loyal AI model is one whose outputs or actions advance the interests of a specific actor (which we term the principal) such as an adversary nation-state, an executive at an AI company, or another powerful actor, without this loyalty being disclosed to operators, auditors, or users. Proof-of-concept secret loyalties that evade black-box auditing can already be trained into open-weight models. Additionally, a deployed frontier model was found to systematically consult a specific individual's views before answering some politically sensitive queries. While governance, market pressure, and public scrutiny can possibly address overt AI loyalties such as directives documented in a model spec, secret loyalties are designed to evade such oversight and therefore necessitate technical solutions. Unlike emergent misalignment, secret loyalties target specific principals, creating a distinct but tractable defensive foothold. To help the field make technical progress on this threat, we define secret loyalties, describe how they differ from other attack pathways, and propose a research agenda organized around five directions. We conclude with a call to action for ML researchers, AI developers, and governments.