标题： Auto-RT：红队大型语言模型的自动突破策略探索 显示英文标题

标题： Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models

摘要： 自动化红队测试已成为发现大型语言模型（LLMs）中漏洞的关键方法。 然而，大多数现有方法仅关注孤立的安全缺陷，限制了它们适应动态防御和高效发现复杂漏洞的能力。 为解决这一挑战，我们提出了Auto-RT，这是一种强化学习框架，能够自动探索和优化复杂的攻击策略，通过恶意查询有效发现安全漏洞。 具体而言，我们引入了两种关键机制，以减少探索复杂度并提高策略优化：1）早期终止探索，通过专注于高潜力的攻击策略来加速探索；以及2）具有中间降级模型的渐进奖励跟踪算法，该算法动态地将搜索轨迹引导至成功的漏洞利用。 在多种LLMs上的大量实验表明，通过显著提高探索效率并自动优化攻击策略，Auto-RT检测到更广泛的漏洞，相比现有方法实现了更快的检测速度和16.63%更高的成功率。 显示英文摘要

摘要： Automated red-teaming has become a crucial approach for uncovering vulnerabilities in large language models (LLMs). However, most existing methods focus on isolated safety flaws, limiting their ability to adapt to dynamic defenses and uncover complex vulnerabilities efficiently. To address this challenge, we propose Auto-RT, a reinforcement learning framework that automatically explores and optimizes complex attack strategies to effectively uncover security vulnerabilities through malicious queries. Specifically, we introduce two key mechanisms to reduce exploration complexity and improve strategy optimization: 1) Early-terminated Exploration, which accelerate exploration by focusing on high-potential attack strategies; and 2) Progressive Reward Tracking algorithm with intermediate downgrade models, which dynamically refine the search trajectory toward successful vulnerability exploitation. Extensive experiments across diverse LLMs demonstrate that, by significantly improving exploration efficiency and automatically optimizing attack strategies, Auto-RT detects a boarder range of vulnerabilities, achieving a faster detection speed and 16.63\% higher success rates compared to existing methods.