ConsCS: Effective and Efficient Verification of Circom Circuits

Circom is a popular programming language for writing arithmetic circuits that can be used to generate zero-knowledge proofs (ZKPs) like zk-SNARKS. ZKPs have received tremendous attention in protocols like zkRollups. The Circom circuits are compiled to Rank-1 Constraint Systems (R1CS) circuits, based...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings / International Conference on Software Engineering s. 616 - 628
Hlavní autoři: Jiang, Jinan, Peng, Xinghao, Chu, Jinzhao, Luo, Xiapu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.04.2025
Témata:
Circuits
Cognition
Computer languages
Data mining
Engines
Protocols
Security
Software algorithms
Software engineering
Writing
ISSN:1558-1225
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Circom is a popular programming language for writing arithmetic circuits that can be used to generate zero-knowledge proofs (ZKPs) like zk-SNARKS. ZKPs have received tremendous attention in protocols like zkRollups. The Circom circuits are compiled to Rank-1 Constraint Systems (R1CS) circuits, based on which zk-SNARK proofs are generated. However, one major challenge associated with R1CS circuits is the problem of under-constrained circuits, which are susceptible to allowing incorrect computations to pass verification due to insufficient constraints, potentially leading to security vulnerabilities. In this paper, we propose a novel framework CONSCS to automatically verify Circom circuits. Our contributions are threefold: 1) we propose novel circuit inference rules to help reduce the size of circuits and to extract more comprehensive information than existing works; 2) we introduce the novel Binary Property Graph (BPG) as a highly efficient reasoning engine, outperforming all existing tools in effectiveness and efficiency; 3) we leverage fine-grained domain-specific information to guide the SMT solving to address non-linear constraints, increasing the success rate of SMT queries of existing works from 2.68% to 48.84%. We conduct experiments to show that CONSCS enhances the solved rate of existing works from around 50-60% to above 80%.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00200