Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large
  Language Models
Authors: name: Yanjiang Liu, name: Shuhen Zhou, name: Yaojie Lu, name: Huijia Zhu, name: Weiqiang Wang, name: Hongyu Lin, name: Ben He, name: Xianpei Han, name: Le Sun

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.