Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation

• URL: http://arxiv.org/abs/2412.13705v1
• Date: Wed, 18 Dec 2024 10:49:41 GMT
• Title: Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation
• Authors: Minkyoung Kim, Yunha Kim, Hyeram Seo, Heejung Choi, Jiye Han, Gaeun Kee, Soyoung Ko, HyoJe Jung, Byeolhee Kim, Young-Hak Kim, Sanghyun Park, Tae Joon Jun,
• Abstract summary: Large language models (LLMs) have exhibited outstanding performance in natural language processing tasks.<n> adversarial attacks in which slight input perturbations can lead to harmful or misleading outputs.<n> gradient-based defensive suffix generation algorithm is designed to bolster the robustness of LLMs.
• Score: 2.3080718283523827
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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, the perplexity score of Gemma-7B decreased from 6.57 to 3.93 when applying the defensive suffix generated by openELM-270M. Furthermore, TruthfulQA evaluations demonstrate consistent improvements with Truthfulness scores increasing by up to 10\% across tested configurations. This approach significantly enhances the security of LLMs in critical applications without requiring extensive retraining.

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