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As blockchain smart contracts become more widespread and carry more valuabledigital assets, they become an increasingly attractive target for attackers.Over the past few years, smart contracts have been subject to a plethora ofdevastating attacks, resulting in billions of dollars in financial losses.There has been a notable surge of research interest in identifying defects insmart contracts. However, existing smart contract fuzzing tools are stillunsatisfactory. They struggle to screen out meaningful transaction sequencesand specify critical inputs for each transaction. As a result, they can onlytrigger a limited range of contract states, making it difficult to unveilcomplicated vulnerabilities hidden in the deep state space.  In this paper, we shed light on smart contract fuzzing by employing asequence-aware mutation and seed mask guidance strategy. In particular, wefirst utilize data-flow-based feedback to determine transaction orders in ameaningful way and further introduce a sequence-aware mutation technique toexplore deeper states. Thereafter, we design a mask-guided seed mutationstrategy that biases the generated transaction inputs to hit target branches.In addition, we develop a dynamic-adaptive energy adjustment paradigm thatbalances the fuzzing resource allocation during a fuzzing campaign. Weimplement our designs into a new smart contract fuzzer named MuFuzz, andextensively evaluate it on three benchmarks. Empirical results demonstrate thatMuFuzz outperforms existing tools in terms of both branch coverage and bugfinding. Overall, MuFuzz achieves higher branch coverage than state-of-the-artfuzzers (up to 25%) and detects 30% more bugs than existing bug detectors.

MuFuzz: Sequence-Aware Mutation and Seed Mask Guidance for Blockchain Smart Contract Fuzzing