Toward Open Weight Models Without Risks: Separating Public and Private Capabilities in LLMs
Open-weight Large Language Models (LLMs) enable scientific progress and broad deployment. However, they make it difficult to control access to sensitive capabilities. Current practice either suppresses dangerous capabilities before release or mediates access through closed services that use specialized model variants, input/output monitors, and API permissions. The former is susceptible to jailbreaks while sacrificing capability for all users to mitigate the risks posed by a few, and the latter is fundamentally incompatible with open-weight release. In this paper, we propose Tiered Language Models (TLMs), where a single set of released weights supports multiple capability levels. In its d...
| Authors | Charbel El Feghali, Arkil Patel, Nicholas Meade et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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