  Although safely enhanced Large Language Models (LLMs) have achieved
remarkable success in tackling various complex tasks in a zero-shot manner,
they remain susceptible to jailbreak attacks, particularly the unknown
jailbreak attack. To enhance LLMs' generalized defense capabilities, we propose
a two-stage adversarial tuning framework, which generates adversarial prompts
to explore worst-case scenarios by optimizing datasets containing pairs of
adversarial prompts and their safe responses. In the first stage, we introduce
the hierarchical meta-universal adversarial prompt learning to efficiently and
effectively generate token-level adversarial prompts. In the second stage, we
propose the automatic adversarial prompt learning to iteratively refine
semantic-level adversarial prompts, further enhancing LLM's defense
capabilities. We conducted comprehensive experiments on three widely used
jailbreak datasets, comparing our framework with six defense baselines under
five representative attack scenarios. The results underscore the superiority of
our proposed methods. Furthermore, our adversarial tuning framework exhibits
empirical generalizability across various attack strategies and target LLMs,
highlighting its potential as a transferable defense mechanism.
