@inproceedings{fonseca-etal-2025-safenudge,
    title = "{SAFENUDGE}: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs",
    author = "Fonseca, Joao  and
      Bell, Andrew  and
      Stoyanovich, Julia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1010/",
    pages = "19966--19980",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model, highlighting the critical need to safeguard widely-deployed models. Safeguarding approaches, which include fine-tuning models or having LLMs ``self-reflect,'' may lengthen the inference time of a model, incur a computational penalty, reduce the semantic fluency of an output, and restrict ``normal'' model behavior. Importantly, these Safety-Performance Trade-offs (SPTs) remain an understudied area. In this work, we make three contributions: (1) We introduce SAFENUDGE, a novel safeguard that combines Controlled Text Generation and ``nudging.'' SAFENUDGE triggers during text-generation while a jailbreak attack is being executed, and can reduce successful jailbreak attempts by between 28.1{\%} and 37.3{\%} by guiding the LLM towards a safe response. It adds minimal latency to inference and has a negligible impact on the semantic fluency of outputs. Second, it supports tunable SPTs, meaning practitioners can set their own tolerance for trade-offs balancing safety and restrictions to normal model behavior. Third, we release the source code for SAFENUDGE at https://github.com/joaopfonseca/SafeNudge. It is open source and compatible with the HuggingFace transformers library."
}