@inproceedings{he-etal-2026-locket,
    title = "Locket: Robust Feature-Locking Technique for Language Models",
    author = "He, Lipeng  and
      Duddu, Vasisht  and
      Asokan, N.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.626/",
    pages = "13770--13784",
    ISBN = "979-8-89176-390-6",
    abstract = "Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for free users and advanced models to paying subscribers. However, this approach is unprofitable and inflexible. A pay-to-unlock scheme for premium features (e.g., math, coding) offers a more sustainable alternative. Enabling such a scheme requires a feature-locking technique (FLoTE) that 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 clients. Existing FLoTEs (e.g., password-locked models) fail to meet these criteria. To fill this gap, we present Locket, a more *robust and scalable* FLoTE to enable pay-to-unlock schemes. We develop a framework for adversarial training and merging of feature-locking *adapters*, which enables Locket to selectively disable specific features of a model. Evaluation shows that Locket is effective (100{\%} refusal rate), utility-preserving ($\leq$ 7{\%} utility degradation), robust ($\leq$ 5{\%} attack success rate), and scalable to multiple features and clients."
}