You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense
Wuyuao Mai, Geng Hong, Pei Chen, Xudong Pan, Baojun Liu, Yuan Zhang, Haixin Duan, Min Yang · 2025
With the rise of generative large language models (LLMs) like LLaMA and ChatGPT, these models have significantly transformed daily life and work by providing advanced insights. However, as jailbreak attacks continue to circumvent built-in safety mechanisms, exploiting carefully crafted scenarios or tokens, the safety risks of LLMs have come into focus. While numerous defense strategies-such as prompt detection, modification, and model fine-tuning-have been proposed to counter these attacks, a critical question arises: do these defenses compromise the utility and usability of LLMs for legitimate users? Existing research predominantly focuses on the effectiveness of defense strategies without thoroughly examining their impact on performance, leaving a gap in understanding the trade-offs between LLM safety and performance.