36 citations · 91 across the 8 of their papers we have counts for
8 papers
JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills
Xiaoyu Wen, Jiajia Li, Zhida He +11
Automated red-teaming has produced a growing collection of attack strategies, yet they typically remain scattered across prompts and workflows, making them difficult to systematica…
Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking
Zhida He, Xiaoyu Wen, Han Qi +7
Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based multi-turn jailbreak method…
SAJA: A State-Action Joint Attack Framework on Multi-Agent Deep Reinforcement Learning
Weiqi Guo, Guanjun Liu, Ziyuan Zhou
Multi-Agent Deep Reinforcement Learning (MADRL) has shown potential for cooperative and competitive tasks such as autonomous driving and strategic gaming. However, models trained b…
PNAct: Crafting Backdoor Attacks in Safe Reinforcement Learning
Weiran Guo, Guanjun Liu, Ziyuan Zhou +1
Reinforcement Learning (RL) is widely used in tasks where agents interact with an environment to maximize rewards. Building on this foundation, Safe Reinforcement Learning (Safe RL…
Enhancing the Robustness of QMIX against State-adversarial Attacks
Weiran Guo, Guanjun Liu, Ziyuan Zhou +2
Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concent…
Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents
Ziyuan Zhou, Guanjun Liu
Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to…
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges
Ziyuan Zhou, Guanjun Liu, Ying Tang
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-st…
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention
Min Yang, Guanjun Liu, Ziyuan Zhou
Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the s…