Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

ArXi:2506.03627v2 Announce Type: replace-cross Large Language Models (LLMs) have nstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to input perturbations, such as typographical errors or slight character order errors, which can significantly impair their performance. Despite advances in prompting techniques such as Chain-of-Thought and automatic prompt generation, developing a prompting strategy that explicitly mitigates the negative impact of such perturbations remains an open challenge.