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Coverage-based greybox fuzzing (CGF) is one of the most successful methods for automated vulnerability detection. Given a seed file (as a sequence of bits), CGF randomly flips, deletes or bits to generate new files. CGF iteratively constructs (and fuzzes) a seed corpus by retaining those generated files which enhance coverage. However, random bitflips are unlikely to produce valid files (or valid chunks in files), for applications processing complex file formats.In this work, we introduce smart greybox fuzzing (SGF) which leverages a high-level structural representation of the seed file to generate new files. We define innovative mutation operators that work on the virtual file structure rather than on the bit level which allows SGF to explore completely new input domains while maintaining file validity. We introduce a novel validity-based power schedule that enables SGF to spend more time generating files that are more likely to pass the parsing stage of the program, which can expose vulnerabilities much deeper in the processing logic.Our evaluation demonstrates the effectiveness of SGF. On several libraries that parse structurally complex files, our tool AFLSMART explores substantially more paths (up to 200%) and exposes more vulnerabilities than baseline AFL. Our tool AFLSMART has discovered 42 zero-day vulnerabilities in widely-used, well-tested tools and libraries; so far 17 CVEs were assigned.

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Intuitively we know, some software errors are more complex than others. If the error can be fixed by changing one faulty statement, it is a simple error. The more substantial the fix must be, the more complex we consider the error.In this work, we formally define and quantify the complexity of an error w.r.t. the complexity of the error's least complex, correct fix. As a concrete measure of complexity for such fixes, we introduce Cyclomatic Change Complexity which is inspired by existing program complexity metrics.Moreover, we introduce CoREBench, a collection of 70 regression errors systematically extracted from several opensource C-projects and compare their complexity with that of the seeded errors in the two most popular error benchmarks, SIR and the Siemens Suite. We find that seeded errors are significantly less complex, i.e., require significantly less substantial fixes, compared to actual regression errors. For example, among the seeded errors more than 42% are simple compared to 8% among the actual ones. This is a concern for the external validity of studies based on seeded errors and we propose CoREBench for the controlled study of regression testing, debugging, and repair techniques.