Targeted greybox fuzzing with static lookahead analysis
| Autor: | Valentin Wüstholz, Maria Christakis |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Schedule Computer Science - Cryptography and Security business.industry Computer science Semantics (computer science) 020207 software engineering 02 engineering and technology Fuzz testing Static analysis Machine learning computer.software_genre Domain (software engineering) Software Engineering (cs.SE) Set (abstract data type) Computer Science - Software Engineering 020204 information systems 0202 electrical engineering electronic engineering information engineering Test suite Artificial intelligence Instrumentation (computer programming) business Cryptography and Security (cs.CR) computer |
| Zdroj: | ICSE |
| DOI: | 10.1145/3377811.3380388 |
| Popis: | Automatic test generation typically aims to generate inputs that explore new paths in the program under test in order to find bugs. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely to contain bugs by using an offline static analysis. In this paper, we introduce a novel technique for targeted greybox fuzzing using an online static analysis that guides the fuzzer toward a set of target locations, for instance, located in recently modified parts of the program. This is achieved by first semantically analyzing each program path that is explored by an input in the fuzzer's test suite. The results of this analysis are then used to control the fuzzer's specialized power schedule, which determines how often to fuzz inputs from the test suite. We implemented our technique by extending a state-of-the-art, industrial fuzzer for Ethereum smart contracts and evaluate its effectiveness on 27 real-world benchmarks. Using an online analysis is particularly suitable for the domain of smart contracts since it does not require any code instrumentation---adding instrumentation to contracts changes their semantics. Our experiments show that targeted fuzzing significantly outperforms standard greybox fuzzing for reaching 83% of the challenging target locations (up to 14x of median speed-up). |
| Databáze: | OpenAIRE |
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