Evaluating the correctness and safety of hBFT with ByzzFuzz

Bachelor Thesis (2025)
Author(s)

A.B. Birke (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Burcu Kulahcioglu Ozkan – Mentor (TU Delft - Software Engineering)

J.M. Louro Neto – Mentor (TU Delft - Software Engineering)

Jérémie Decouchant – Graduation committee member (TU Delft - Data-Intensive Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2025
Language
English
Graduation Date
31-01-2025
Awarding Institution
Delft University of Technology
Project
['CSE3000 Research Project']
Programme
['Computer Science and Engineering']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Byzantine Fault Tolerant (BFT) protocols are designed to achieve consensus even in the presence of Byzantine faults. Although BFT protocols provide strong theoretical guarantees, bugs in the implementation of the protocols can allow for malicious activity. While previous work, like Twins and Tyr, has focused on alternative methods to test BFT protocols, our work builds upon ByzzFuzz, an automated testing algorithm, which has previously identified bugs in protocols like Tendermint and Ripple. Despite its success, its effectiveness has not yet been tested on speculative BFT protocols like hBFT. This research evaluates the effectiveness of ByzzFuzz in assessing the correctness and safety of hBFT. To address this, we implemented hBFT in ByzzBench, a comprehensive framework where BFT protocols can be evaluated using ByzzFuzz and other testing algorithms. Through testing, ByzzFuzz successfully uncovered a potential violation in hBFT and detected an injected bug in the hBFT implementation. However, detecting the known violation of hBFT required a controlled environment, highlighting areas where ByzzFuzz could be improved. The findings show that ByzzFuzz is effective at identifying bugs in hBFT, while also suggesting the need for improvement to enhance its robustness and adaptability.

Files

CSE3000_Final_Report_v2.pdf
(pdf | 0.399 Mb)
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