1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
Self-Destructive Language Model
Yuhui Wang, Rongyi Zhu, Ting Wang
Harmful fine-tuning attacks pose a major threat to the security of large language models (LLMs), allowing adversaries to compromise safety guardrails with minimal harmful data. Whi…
AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models
Jiacheng Liang, Tanqiu Jiang, Yuhui Wang +3
This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its core, AutoRAN pioneers an execution…
GraphRAG under Fire
Jiacheng Liang, Yuhui Wang, Changjiang Li +4
GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context a…
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
Wei Shao, Rongyi Zhu, Cai Yang +7
Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions. Recent advancements have sparked a growin…
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Xiao-Yang Liu, Rongyi Zhu, Daochen Zha +4
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical scien…