adversarial-defense

Researchers from Southampton and Manchester propose a hybrid adversarial defence framework for LLMs that combines entropy-based, uncertainty-based, and geometric-based models to simultaneously address hallucination and adversarial vulnerability in NLU tasks, achieving up to 64.92% improvement in adversarial robustness and 62.27% reduction in attack success rate.

This paper proposes methods for protecting large language models against unauthorized knowledge distillation by rewriting reasoning traces to degrade training usefulness while preserving correctness, and embedding verifiable watermarks in distilled student models. The approach uses instruction-based and gradient-based rewriting techniques to achieve anti-distillation effects without compromising teacher model performance.