Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM

• URL: http://arxiv.org/abs/2309.14348v3
• Date: Wed, 12 Jun 2024 00:27:06 GMT
• Title: Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
• Authors: Bochuan Cao, Yuanpu Cao, Lu Lin, Jinghui Chen,
• Abstract summary: We introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks. RA-LLM can successfully defend against both state-of-the-art adversarial prompts and popular handcrafted jailbreaking prompts.
• Score: 23.16217797677075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Large Language Models (LLMs) have made significant advancements and are now widely used across various domains. Unfortunately, there has been a rising concern that LLMs can be misused to generate harmful or malicious content. Though a line of research has focused on aligning LLMs with human values and preventing them from producing inappropriate content, such alignments are usually vulnerable and can be bypassed by alignment-breaking attacks via adversarially optimized or handcrafted jailbreaking prompts. In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks. RA-LLM can be directly constructed upon an existing aligned LLM with a robust alignment checking function, without requiring any expensive retraining or fine-tuning process of the original LLM. Furthermore, we also provide a theoretical analysis for RA-LLM to verify its effectiveness in defending against alignment-breaking attacks. Through real-world experiments on open-source large language models, we demonstrate that RA-LLM can successfully defend against both state-of-the-art adversarial prompts and popular handcrafted jailbreaking prompts by reducing their attack success rates from nearly 100% to around 10% or less.

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