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A variety of solutions, e.g., Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Burn (PoB), and Proof-of-Elapsed-Time (PoET), have been proposed to make consensus mechanism used by the blockchain technology more democratic, efficient, and scalable. However, these solutions have a number of limitations, e.g., PoW approach requires a huge amount of computational power, scales poorly, and wastes a lot of electrical energy. Recently, an innovative protocol called Algorand has been proposed to overcome these limitations. Algorand not only guarantees an overwhelming probability of linearity of the blockchain, but it also aims to solve the "blockchain trilemma" of decentralization, scalability, and security.
In this paper, we present a security analysis of Algorand. To the best of our knowledge, it is the first security analysis as well as the first formal study on Algorand. We designed an attack scenario in which a group of malicious users tries to break the protocol, or at least limit it to a reduced partition of network users, by exploiting a security flaw in the messages validation process of the Byzantine Agreement (BA). Since the source code or an official simulator for Algorand was not available at the time of our study, we created a simulator (which is available on request) to implement the protocol and assess the feasibility of our attack scenario. Our attack requires the attacker to merely have the trivial capability of establishing multiple connections with targeted nodes, and it costs practically nothing to the attacker. Our results show that it is possible to slow down the message validation process on honest nodes - which eventually forces them to select default values on the consensus - leaving the targeted nodes behind in the chain as compared to the non-attacked nodes. Even though our results are subject to the real implementation of the protocol, the core concept of our attack remains valid.
Conference: Proceedings of the 14th International Conference on Availability, Reliability and Security - ARES '19.
Date of Publication: August 2019.
Publisher: ACM Digital Library.