Fuzzing Processing Pipelines for Zero-Knowledge Circuits
- URL: http://arxiv.org/abs/2411.02077v1
- Date: Mon, 04 Nov 2024 13:31:03 GMT
- Title: Fuzzing Processing Pipelines for Zero-Knowledge Circuits
- Authors: Christoph Hochrainer, Anastasia Isychev, Valentin Wüstholz, Maria Christakis,
- Abstract summary: We present the first systematic fuzzing technique for Zero-knowledge (ZK) pipelines.
This technique uses metamorphic test oracles to detect critical logic bugs.
We have implemented our technique in an open-source tool called Circuzz.
- Score: 1.9749268648715583
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Zero-knowledge (ZK) protocols have recently found numerous practical applications, such as in authentication, online-voting, and blockchain systems. These protocols are powered by highly complex pipelines that process deterministic programs, called circuits, written in one of many domain-specific programming languages, e.g., Circom, Noir, and others. Logic bugs in circuit-processing pipelines could have catastrophic consequences and cause significant financial and reputational damage. As an example, consider that a logic bug in a ZK pipeline could result in attackers stealing identities or assets. It is, therefore, critical to develop effective techniques for checking their correctness. In this paper, we present the first systematic fuzzing technique for ZK pipelines, which uses metamorphic test oracles to detect critical logic bugs. We have implemented our technique in an open-source tool called Circuzz. We used Circuzz to test four significantly different ZK pipelines and found a total of 16 logic bugs in all pipelines. Due to their critical nature, 15 of our bugs have already been fixed by the pipeline developers.
Related papers
- Towards Fuzzing Zero-Knowledge Proof Circuits (Short Paper) [1.6822770693792823]
Zero-knowledge proofs (ZKPs) have evolved from a theoretical cryptographic concept into a powerful tool for implementing privacy-preserving and verifiable applications without requiring trust assumptions.
We discuss the challenges of applying fuzzing to ZKP circuits, examine the oracle problem and its potential solutions, and propose techniques for input generation and test harness construction.
We demonstrate that fuzzing can be effective in this domain by implementing a fuzzer for textttzk-regex, a cornerstone library in modern ZKP applications.
arXiv Detail & Related papers (2025-04-21T06:19:06Z) - zkFuzz: Foundation and Framework for Effective Fuzzing of Zero-Knowledge Circuits [24.179342690266523]
ZK circuits enable privacy-preserving computations and are central to many cryptographic protocols.
Existing tools overlook several critical behaviors, such as intermediate computations and program aborts.
We present zkFuzz, a novel program mutation-based fuzzing framework for detecting TCCT violations.
arXiv Detail & Related papers (2025-04-16T10:43:48Z) - Practical Pipeline-Aware Regression Test Optimization for Continuous Integration [9.079940595000087]
Continuous Integration (CI) is commonly applied to ensure consistent code quality.
Developers commonly split test executions across multiple pipelines, running small and fast tests in pre-submit stages while executing long-running and flaky tests in post-submit pipelines.
We developed a lightweight and pipeline-aware regression test optimization approach that employs Reinforcement Learning models trained on language-agnostic features.
arXiv Detail & Related papers (2025-01-20T15:39:16Z) - Demonstrating real-time and low-latency quantum error correction with superconducting qubits [52.08698178354922]
We demonstrate low-latency feedback with a scalable FPGA decoder integrated into a superconducting quantum processor.
We observe logical error suppression as the number of decoding rounds is increased.
The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments.
arXiv Detail & Related papers (2024-10-07T17:07:18Z) - Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
We propose a complex reasoning schema over KG upon large language models (LLMs)
We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs.
Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
arXiv Detail & Related papers (2024-05-02T18:12:08Z) - AC4: Algebraic Computation Checker for Circuit Constraints in ZKPs [4.810904298160317]
Underconstrained or overconstrained circuits may lead to bugs.
A tool, AC4, is proposed to represent the implementation of the method.
Within a solvable range, the checking time has also exhibited noticeable improvement.
arXiv Detail & Related papers (2024-03-23T01:44:57Z) - Patch2QL: Discover Cognate Defects in Open Source Software Supply Chain
With Auto-generated Static Analysis Rules [1.9591497166224197]
We propose a novel technique for detecting cognate defects in OSS through the automatic generation of SAST rules.
Specifically, it extracts key syntax and semantic information from pre- and post-patch versions of code.
We have implemented a prototype tool called Patch2QL and applied it to fundamental OSS in C/C++.
arXiv Detail & Related papers (2024-01-23T02:23:11Z) - LINC: A Neurosymbolic Approach for Logical Reasoning by Combining
Language Models with First-Order Logic Provers [60.009969929857704]
Logical reasoning is an important task for artificial intelligence with potential impacts on science, mathematics, and society.
In this work, we reformulating such tasks as modular neurosymbolic programming, which we call LINC.
We observe significant performance gains on FOLIO and a balanced subset of ProofWriter for three different models in nearly all experimental conditions we evaluate.
arXiv Detail & Related papers (2023-10-23T17:58:40Z) - HOPPER: Interpretative Fuzzing for Libraries [6.36596812288503]
HOPPER can fuzz libraries without requiring any domain knowledge.
It transforms the problem of library fuzzing into the problem of interpreter fuzzing.
arXiv Detail & Related papers (2023-09-07T06:11:18Z) - Fuzzing with Quantitative and Adaptive Hot-Bytes Identification [6.442499249981947]
American fuzzy lop, a leading fuzzing tool, has demonstrated its powerful bug finding ability through a vast number of reported CVEs.
We propose an approach called toolwhich is designed based on the following principles.
Our evaluation results on 10 real-world programs and LAVA-M dataset show that toolachieves sustained increases in branch coverage and discovers more bugs than other fuzzers.
arXiv Detail & Related papers (2023-07-05T13:41:35Z) - Fact-Checking Complex Claims with Program-Guided Reasoning [99.7212240712869]
Program-Guided Fact-Checking (ProgramFC) is a novel fact-checking model that decomposes complex claims into simpler sub-tasks.
We first leverage the in-context learning ability of large language models to generate reasoning programs.
We execute the program by delegating each sub-task to the corresponding sub-task handler.
arXiv Detail & Related papers (2023-05-22T06:11:15Z) - Overcoming leakage in scalable quantum error correction [128.39402546769284]
Leakage of quantum information out of computational states into higher energy states represents a major challenge in the pursuit of quantum error correction (QEC)
Here, we demonstrate the execution of a distance-3 surface code and distance-21 bit-flip code on a Sycamore quantum processor where leakage is removed from all qubits in each cycle.
We report a ten-fold reduction in steady-state leakage population on the data qubits encoding the logical state and an average leakage population of less than $1 times 10-3$ throughout the entire device.
arXiv Detail & Related papers (2022-11-09T07:54:35Z) - Fault-Aware Neural Code Rankers [64.41888054066861]
We propose fault-aware neural code rankers that can predict the correctness of a sampled program without executing it.
Our fault-aware rankers can significantly increase the pass@1 accuracy of various code generation models.
arXiv Detail & Related papers (2022-06-04T22:01:05Z) - Fault-tolerant operation of a logical qubit in a diamond quantum
processor [0.21670084965090575]
We demonstrate fault-tolerant operations on a logical qubit using spin qubits in diamond.
Our realization of fault-tolerant protocols on the logical-qubit level is a key step towards large-scale quantum information processing.
arXiv Detail & Related papers (2021-08-03T17:39:25Z) - Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution
Pipeline [86.01209981642005]
We study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Our suggested pipeline DN$to$SR$to$DM yields consistently better performance than other sequential pipelines.
We propose an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves state-of-the-art performance for the mixture problem.
arXiv Detail & Related papers (2019-05-07T13:19:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.