Wed 6 Dec 2023 11:15 - 11:30 at Golden Gate C3 - Program Analysis II Chair(s): Nico Rosner

Smart contracts play an increasingly important role in Ethereum platform. It provides various functions implementing numerous services, whose bytecode runs on Ethereum Virtual Machine. To use services by invoking corresponding functions, the callers need to know the function signatures. Moreover, such signatures provide crucial information for many downstream applications, e.g., identifying smart contracts, fuzzing, detecting vulnerabilities, etc. However, it is challenging to infer function signatures from the bytecode due to a lack of type information. Existing work solving this problem depended heavily on limited databases or hard-coded heuristic patterns. However, these approaches are hard to be adapted to semantic differences in distinct languages and various compiler versions when developing smart contracts. In this paper, we propose a novel framework DeepInfer that first leverages deep learning techniques to automatically infer function signatures and returns. The novelties of DeepInfer are: 1) DeepInfer lifts the bytecode into the Intermediate Representation (IR) to preserve code semantics; 2) DeepInfer extracts the type-related knowledge (e.g., critical data flows, constant values, and control flow graphs) from the IR to recover function signatures and returns. We conduct experiments on Solidity and Vyper smart contracts and the results show that DeepInfer performs faster and more accurate than existing tools, while being immune to changes in different languages and various compiler versions.

Wed 6 Dec

Displayed time zone: Pacific Time (US & Canada) change

11:00 - 12:30
Program Analysis IIResearch Papers / Journal First at Golden Gate C3
Chair(s): Nico Rosner Amazon Web Services
11:00
15m
Talk
[Remote] OOM-Guard: Towards Improving The Ergonomics of Rust OOM Handling via A Reservation-based Approach
Research Papers
Chengjun Chen Fudan University; Ant Group, Zhicong Zhang Fudan University, Hongliang Tian Ant Group, Shoumeng Yan Ant Group, Hui Xu Fudan University
Media Attached
11:15
15m
Talk
[Remote] DeepInfer: Deep Type Inference from Smart Contract Bytecode
Research Papers
Kunsong Zhao The Hong Kong Polytechnic University, zihao li The Hong Kong Polytechnic Universituy, Jianfeng Li Xi’an Jiaotong University, He Ye KTH Royal Institute of Technology, Xiapu Luo Hong Kong Polytechnic University, Ting Chen University of Electronic Science and Technology of China
Media Attached
11:30
15m
Talk
Statistical Type Inference for Incomplete Programs
Research Papers
Yaohui Peng School of Computer Science, Wuhan University, Jing Xie School of Computer Science, Wuhan University, Qiongling Yang School of Computer Science, Wuhan University, Hanwen Guo School of Computer Science, Wuhan University, Qingan Li School of Computer Science, Wuhan University, Jingling Xue School of Computer Science and Engineering, UNSW Sydney, YUAN Mengting School of Computer Science, Wuhan University, Wuhan, China
Media Attached
11:45
15m
Talk
Demystifying Hidden Sensitive Operations in Android apps
Journal First
Xiaoyu Sun Australian National University, Australia, Xiao Chen Monash University, Li Li Beihang University, Haipeng Cai Washington State University, John Grundy Monash University, Jordan Samhi University of Luxembourg, Tegawendé F. Bissyandé University of Luxembourg, Jacques Klein University of Luxembourg
Media Attached
12:00
15m
Talk
DeMinify: Neural Variable Name Recovery and Type Inference
Research Papers
Yi Li New Jersey Institute of Technology, Aashish Yadavally The University of Texas at Dallas, Jiaxing Zhang New Jersey Institute of Technology, Shaohua Wang Central University of Finance and Economics , Tien N. Nguyen University of Texas at Dallas
Media Attached