Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large
  Language Models
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.
677c948a6f370093aa2a764e
Yanjiang Liu
677c948a6f370093aa2a764f
Shuhen Zhou
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Yaojie Lu
admin_assigned
2025-01-07T13:24:42.703Z
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Yaojie Lu
user
luyaojie
677c948a6f370093aa2a7651
Huijia Zhu
677c948a6f370093aa2a7652
Weiqiang Wang
admin_assigned
2025-01-07T13:24:28.891Z
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weyl
user
weyl
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Hongyu Lin
admin_assigned
2025-01-07T13:24:22.844Z
6711c702f858a456b4b9f3a4
/avatars/178e9567c3111ab22717c3c0dd003a6a.svg
Hongyu  Lin
user
sanmusunrise
677c948a6f370093aa2a7654
Ben He
677c948a6f370093aa2a7655
Xianpei Han
admin_assigned
2025-01-07T13:24:12.963Z
65e99a77e71555ed193609cf
/avatars/38ceb127883944677665da967d17dd18.svg
Xianpei Han
user
xphan
677c948a6f370093aa2a7656
Le Sun
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large
  Language Models
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.
Auto-RT, a reinforcement learning framework, enhances the detection of complex vulnerabilities in large language models by optimizing attack strategies through early exploration and progressive reward tracking.
reinforcement learning Auto-RT Early-terminated Exploration Progressive Reward Tracking intermediate downgrade models vulnerability exploitation large language models attack strategies exploration efficiency success rates