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We present a novel gray-box fuzzing algorithm monitoring executions ofinstructions converting numerical values to Boolean ones. An important class ofsuch instructions evaluate predicates, e.g., *cmp in LLVM. That alone allows usto infer the input dependency (c.f. the taint analysis) during the fuzzingon-the-fly with reasonable accuracy, which in turn enables an effective use ofthe gradient descent on these instructions (to invert the result of theirevaluation). Although the fuzzing attempts to maximize the coverage of theinstructions, there is an interesting correlation with the standard branchcoverage, which we are able to achieve indirectly. The evaluation on Test-Comp2023 benchmarks shows that our approach, despite being a pure gray-box fuzzing,is able to compete with the leading tools in the competition, which combinefuzzing with other powerful techniques like model checking, symbolic execution,or abstract interpretation.

Gray-Box Fuzzing via Gradient Descent and Boolean Expression Coverage (Technical Report)