2 citations · 3 across the 5 of their papers we have counts for
6 papers
SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator
Xueyang Zhou, Weidong Wang, Lin Lu +7
Large Language Model (LLM)-based agents are increasingly deployed in real-world applications such as "digital assistants, autonomous customer service, and decision-support systems"…
Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging
Lin Lu, Zhigang Zuo, Ziji Sheng +1
Model merging has emerged as a promising approach for updating large language models (LLMs) by integrating multiple domain-specific models into a cross-domain merged model. Despite…
Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack
Chenxi Dai, Lin Lu, Pan Zhou
Decentralized training has become a resource-efficient framework to democratize the training of large language models (LLMs). However, the privacy risks associated with this framew…
Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection
Yuqi Zhou, Lin Lu, Hanchi Sun +2
Jailbreak attacks on large language models (LLMs) involve inducing these models to generate harmful content that violates ethics or laws, posing a significant threat to LLM securit…
AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens
Lin Lu, Hai Yan, Zenghui Yuan +4
Jailbreak attacks in large language models (LLMs) entail inducing the models to generate content that breaches ethical and legal norm through the use of malicious prompts, posing a…
Exploring the Robustness of Decentralized Training for Large Language Models
Lin Lu, Chenxi Dai, Wangcheng Tao +3
Decentralized training of large language models has emerged as an effective way to democratize this technology. However, the potential threats associated with this approach have no…