Mitigating Adversarial Attacks In Llms Through Defensive Suffix Generation

Abstract

Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks. However, these models remain susceptible to adversarial attacks in which slight input perturbations can lead to harmful or misleading outputs. A gradient-based defensive suffix generation algorithm is designed to bolster the robustness of LLMs. By appending carefully optimized defensive suffixes to input prompts, the algorithm mitigates adversarial influences while preserving the models' utility. To enhance adversarial understanding, a novel total loss function ($L_{\{}\text{{}total\}\}$) combining defensive loss ($L_{\{}\text{{}def\}\}$) and adversarial loss ($L_{\{}\text{{}adv\}\}$) generates defensive suffixes more effectively. Experimental evaluations conducted on open-source LLMs such as Gemma-7B, mistral-7B, Llama2-7B, and Llama2-13B show that the proposed method reduces attack success rates (ASR) by an average of 11% compared to models without defensive suffixes. Additionally,