SAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs

AbstractLarge 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.