1 citations · 1 across the 4 of their papers we have counts for
6 papers
Identifying Good and Bad Neurons for Task-Level Controllable LLMs
Wenjie Li, Guansong Pang, Hezhe Qiao +2
Large Language Models have demonstrated remarkable capabilities on multiple-choice question answering benchmarks, but the complex mechanisms underlying their large-scale neurons re…
Bamboo: LLM-Driven Discovery of API-Permission Mappings in the Android Framework
Han Hu, Wei Minn, Yonghui Liu +6
The permission mechanism in the Android Framework is integral to safeguarding the privacy of users by managing users' and processes' access to sensitive resources and operations. A…
Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning
Ye Liu, Yuqing Niu, Chengyan Ma +5
Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging beca…
DITING: A Static Analyzer for Identifying Bad Partitioning Issues in TEE Applications
Chengyan Ma, Ruidong Han, Jieke Shi +7
Trusted Execution Environment (TEE) enhances the security of mobile applications and cloud services by isolating sensitive code in the secure world from the non-secure normal world…
Automated TEE Adaptation with LLMs: Identifying, Transforming, and Porting Sensitive Functions in Programs
Ruidong Han, Zhou Yang, Chengyan Ma +5
Trusted Execution Environments (TEEs) isolate a special space within a device memory that is not accessible to the normal world (also known as the untrusted environment), even when…
Shelving it rather than Ditching it: Dynamically Debloating DEX and Native Methods of Android Applications without APK Modification
Zicheng Zhang, Jiakun Liu, Ferdian Thung +10
Today's Android developers tend to include numerous features to accommodate diverse user requirements, which inevitably leads to bloated apps. Yet more often than not, only a fract…