SolType: Refinement Types for Arithmetic Overflow in Solidity
As smart contracts gain adoption in financial transactions, it becomes increasingly important to ensurethat they are free of bugs and security vulnerabilities. Of particular relevance in this context are arithmetic overflow bugs, as integers are often used to represent financial assets like account balances. Motivated by this observation, this paper presents SolType, a refinement type system for Solidity that can be used toprevent arithmetic over- and under-flows in smart contracts. SolType allows developers to add refinement type annotations and uses them to prove that arithmetic operations do not lead to over- and under-flows. SolType incorporates a rich vocabulary of refinement terms that allow expressing relationships between integer values and aggregate properties of complex data structures. Furthermore, our implementation, called Solid, incorporates a type inference engine and can automatically infer useful type annotations, including non-trivial contract invariants.
To evaluate the usefulness of our type system, we use Solid to prove arithmetic safety of a total of 120 smart contracts. When used in its fully automated mode (i.e., using Solid’s type inference capabilities), Solid is able to eliminate 86.3% of redundant runtime checks used to guard against overflows. We also compare Solid against a state-of-the-art arithmetic safety verifier called VeriSmart and show that Solid has a significantly lower false positive rate, while being significantly faster in terms of verification time.
Thu 20 JanDisplayed time zone: Eastern Time (US & Canada) change
13:30 - 14:45
|On Type-Cases, Union Elimination, and Occurrence TypingInPerson|
Giuseppe Castagna CNRS; Université de Paris, Mickaël Laurent Université de Paris, Kim Nguyễn Université Paris-Saclay, Matthew Lutze Université de ParisDOI Media Attached
|Oblivious Algebraic Data TypesInPerson|
POPLDOI Media Attached
|SolType: Refinement Types for Arithmetic Overflow in SolidityRemote|
Bryan Tan University of California at Santa Barbara, Benjamin Mariano University of Texas at Austin, Shuvendu K. Lahiri Microsoft Research, Işil Dillig University of Texas at Austin, Yu Feng University of California at Santa BarbaraDOI Media Attached