Locket: Robust Feature-Locking Technique for Language Models
Authors: name: Lipeng He, name: Vasisht Duddu, name: N. Asokan
Organization: University of Waterloo

Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to
generate revenue, offering basic models for free users, and advanced models for
paying subscribers. However, a finer-grained pay-to-unlock scheme for premium
features (e.g., math, coding) is thought to be more economically viable for the
providers. Such a scheme requires a feature-locking technique (FLoTE) which is
(i) effective in refusing locked features, (ii) utility-preserving for unlocked
features, (iii) robust against evasion or unauthorized credential sharing, and
(iv) scalable to multiple features and users. However, existing FLoTEs (e.g.,
password-locked models) are not robust or scalable. We present Locket, the
first robust and scalable FLoTE to enable pay-to-unlock schemes. Locket uses a
novel merging approach to attach adapters to an LLM for refusing unauthorized
features. Our comprehensive evaluation shows that Locket is effective (100%
refusal on locked features), utility-preserving (leq 7% utility degradation
in unlocked features), robust (leq 5% attack success rate), and scales to
multiple features and clients.