SNARKProbe: An Automated Security Analysis Framework for zkSNARK Implementations

Principal Investigator: Christina Garman

With the recent growing interest in privacy-enhancing technologies, we are seeing a complementary growth in the desire to build and deploy complex cryptographic systems that involve techniques like zero-knowledge proofs. Of these, general purpose proof systems like zkSNARKs have seen the most interest, due to their small proof size, fast verification, and expressiveness. Unfortunately, as we have seen with many areas of cryptography, guaranteeing correct implementations can be tricky, as the protocols themselves are complicated and often require substantial low-level manual effort to achieve maximum performance. To help with this problem, and gain better assurances about the correctness and security of already implemented zkSNARK protocols and applications, we design and build SNARKProbe, an automated security analysis framework for zkSNARKs that can scan R1CS-based libraries and applications to detect various issues, such as edge case crashing, cryptographic operation errors, and/or inconsistencies with protocol descriptions. SNARKProbe leverages a variety of analysis techniques, including fuzzing and SMT solvers. We test the performance of SNARKProbe on a variety of different experimental parameters to demonstrate its practicality and reasonable runtime, and we also evaluate its ability to find potential inconsistencies and errors in implementations.

Personnel

Students: Yongming Fan