This program is tentative and subject to change.

Wed 23 Oct 2024 14:40 - 15:00 at San Gabriel - Formal Methods 1 Chair(s): Benjamin Delaware

Since funds or tokens in smart contracts are maintained through specific state variables, contract audit, an effective means for security assurance, particularly focuses on these variables and their related operations. However, the absence of publicly accessible source code for numerous contracts, with only bytecode exposed, hinders audit efforts. Recovering variables and their types from Solidity bytecode is thus a critical task in smart contract analysis and audit, yet this is a challenging task because the bytecode loses variable and type information, only with low-level data operated by stack manipulations and untyped memory/storage accesses. The state-of-the-art smart contract decompilers miss identifying many variables and incorrectly infer the types for many identified variables. To this end, we propose \textsf{\textsc{VarLifter}}, a lifter dedicated to the precise and efficient recovery of typed variables. \textsf{\textsc{VarLifter}} interprets every read or written field of a data region as at least one potential variable, and after discarding falsely identified variables, it progressively refines the variable types based on the variable behaviors in the form of operation sequences. We evaluate \textsf{\textsc{VarLifter}} on 34,832 real-world Solidity smart contracts. \textsf{\textsc{VarLifter}} attains a precision of 97.48% and a recall of 91.84% for typed variable recovery. Moreover, \textsf{\textsc{VarLifter}} finishes analyzing 77% of smart contracts in around 10 seconds per contract. If \textsf{\textsc{VarLifter}} is used to replace the variable recovery modules of the two state-of-the-art Solidity bytecode decompilers, 52.4%, and 74.6% more typed variables will be correctly recovered, respectively. The applications of \textsf{\textsc{VarLifter}} to contract decompilation, contract audit, and contract bytecode fuzzing illustrate that the recovered variable information improves many contract analysis tasks.

This program is tentative and subject to change.

Wed 23 Oct

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

13:40 - 15:20
Formal Methods 1OOPSLA 2024 at San Gabriel
Chair(s): Benjamin Delaware Purdue University
13:40
20m
Talk
Realistic Realizability: Specifying ABIs You Can Count On
OOPSLA 2024
Andrew Wagner Northeastern University, Zachary Eisbach Northeastern University, Amal Ahmed Northeastern University, USA
14:00
20m
Talk
AUTOMAP: Inferring Rank-Polymorphic Function Applications with Integer Linear Programming
OOPSLA 2024
Robert Schenck DIKU, University of Copenhagen, Nikolaj Hey Hinnerskov DIKU, University of Copenhagen, Troels Henriksen University of Copenhagen, Magnus Madsen Aarhus University, Martin Elsman University of Copenhagen
14:20
20m
Talk
Outcome Separation Logic: Local Reasoning for Correctness and Incorrectness with Computational Effects
OOPSLA 2024
Noam Zilberstein Cornell University, Angelina Saliling Cornell University, Alexandra Silva Cornell University
14:40
20m
Talk
VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart Contracts
OOPSLA 2024
Yichuan Li Nanjing University of Science and Technology, Wei Song Nanjing University of Science and Technology, Jeff Huang Texas A&M University
15:00
20m
Talk
Weighted Context-Free-Language Ordered Binary Decision Diagrams
OOPSLA 2024
Meghana Aparna Sistla The University of Texas at Austin, Swarat Chaudhuri University of Texas at Austin, Thomas Reps University of Wisconsin-Madison