Language-Agnostic Detection of Bugs in Zero-Knowledge Proof Programs

Host: Greg Zaverucha, Microsoft Research Speaker(s): Arman Kolozyan, Max Planck Institute for Security and Privacy Zero-knowledge proofs (ZKPs) allow a prover to convince a verifier of a statement's truth without revealing any other information. In recent years, ZKPs have matured into a practical technology underpinning major applications. However, implementing ZKP programs remains challenging, as they operate over arithmetic circuits that encode the logic of both the prover and the verifier. Therefore, developers must not only express the computations for generating proofs, but also explicitly specify the constraints for verification. As recent studies have shown, this decoupling may lead to critical ZKP-specific vulnerabilities. Unfortunately, existing tools for detecting them are limited, as they: (1) are tightly coupled to specific ZKP languages, (2) are confined to the constraint level, preventing reasoning about the underlying computations, (3) target only a narrow class of bugs, and (4) suffer from scalability bottlenecks due to reliance on SMT solvers. To address these limitations, we propose a language-agnostic formal model, called the Domain Consistency Model (DCM), which captures the relationship between computations and constraints. Using this model, we provide a taxonomy of vulnerabilities based on computation–constraint mismatches, including novel subclasses overlooked by existing models. Next, we implement a language-agnostic bug detection tool, called CCC-Check, which is based on abstract interpretation. Our evaluation shows that CCC-Check is on average two orders of magnitude faster than SMT-based approaches while achieving comparable precision. Finally, using the DCM, we examine six widely adopted ZKP projects and uncover 15 previously unknown vulnerabilities. We reported these bugs to the projects' maintainers, 13 of which have since been patched. Of these 15 vulnerabilities, 12 could not be captured by existing models.