Over the past two years, the use of large language models (LLMs) has advanced
rapidly. While these LLMs offer considerable convenience, they also raise
security concerns, as LLMs are vulnerable to adversarial attacks by some
well-designed textual perturbations. In this paper, we introduce a novel
defense technique named Large LAnguage MOdel Sentinel (LLAMOS), which is
designed to enhance the adversarial robustness of LLMs by purifying the
adversarial textual examples before feeding them into the target LLM. Our
method comprises two main components: a) Agent instruction, which can simulate
a new agent for adversarial defense, altering minimal characters to maintain
the original meaning of the sentence while defending against attacks; b)
Defense guidance, which provides strategies for modifying clean or adversarial
examples to ensure effective defense and accurate outputs from the target LLMs.
Remarkably, the defense agent demonstrates robust defensive capabilities even
without learning from adversarial examples. Additionally, we conduct an
intriguing adversarial experiment where we develop two agents, one for defense
and one for defense, and engage them in mutual confrontation. During the
adversarial interactions, neither agent completely beat the other. Extensive
experiments on both open-source and closed-source LLMs demonstrate that our
method effectively defends against adversarial attacks, thereby enhancing
adversarial robustness.

我们引入了一种名为 LLAMOS 的新型防御技术，通过净化输入到目标大型语言模型之前的对抗文本示例，以增强大型语言模型的对抗鲁棒性。我们的方法包括两个主要组成部分：a) 代理指示，可以模拟新的代理进行对抗防御，通过最小限度地更改字符来保持句子的原始含义，并防御攻击；b) 防御指导，提供修改干净或对抗性示例以确保有效防御和目标大型语言模型准确输出的策略。通过在开源和闭源大型语言模型上进行广泛实验，我们的方法可以有效抵御对抗性攻击，从而提高对抗鲁棒性。