Improving Function Coverage with Munch: A Hybrid Fuzzing and Directed Symbolic Execution Approach

Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs. Fuzzing, a blackbox method that mutates seed input values, is generally incapable of generating diverse inputs that exercise all paths in the program. Due to the path-explosion problem and dependence on SMT solvers, symbolic execution may also not achieve high path coverage. A hybrid technique involving fuzzing and symbolic execution may achieve better function coverage than fuzzing or symbolic execution alone. In this paper, we present Munch, an open source framework implementing two hybrid techniques based on fuzzing and symbolic execution. We empirically show using nine large open-source programs that overall, Munch achieves higher (in-depth) function coverage than symbolic execution or fuzzing alone. Using metrics based on total analyses time and number of queries issued to the SMT solver, we also show that Munch is more efficient at achieving better function coverage.Comment: To appear at 33rd ACM/SIGAPP Symposium On Applied Computing (SAC). To be held from 9th to 13th April, 201

Towards Smart Hybrid Fuzzing for Smart Contracts

Smart contracts are Turing-complete programs that are executed across a blockchain network. Unlike traditional programs, once deployed they cannot be modified. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In recent years, smart contracts suffered major exploits, costing millions of dollars, due to programming errors. As a result, a variety of tools for detecting bugs has been proposed. However, majority of these tools often yield many false positives due to over-approximation or poor code coverage due to complex path constraints. Fuzzing or fuzz testing is a popular and effective software testing technique. However, traditional fuzzers tend to be more effective towards finding shallow bugs and less effective in finding bugs that lie deeper in the execution. In this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary fuzzing with constraint solving in order to execute more code and find more bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts of a smart contract, while constraint solving is used to generate inputs which satisfy complex conditions that prevent the evolutionary fuzzing from exploring deeper paths. Moreover, we use data dependency analysis to efficiently generate sequences of transactions, that create specific contract states in which bugs may be hidden. We evaluate the effectiveness of our fuzzing strategy, by comparing CONFUZZIUS with state-of-the-art symbolic execution tools and fuzzers. Our evaluation shows that our hybrid fuzzing approach produces significantly better results than state-of-the-art symbolic execution tools and fuzzers