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This paper introduces Pythia, the first fuzzer that augments grammar-basedfuzzing with coverage-guided feedback and a learning-based mutation strategyfor stateful REST API fuzzing. Pythia uses a statistical model to learn commonusage patterns of a target REST API from structurally valid seed inputs. Itthen generates learning-based mutations by injecting a small amount of noisedeviating from common usage patterns while still maintaining syntacticvalidity. Pythia's mutation strategy helps generate grammatically valid testcases and coverage-guided feedback helps prioritize the test cases that aremore likely to find bugs. We present experimental evaluation on threeproduction-scale, open-source cloud services showing that Pythia outperformsprior approaches both in code coverage and new bugs found. Using Pythia, wefound 29 new bugs which we are in the process of reporting to the respectiveservice owners.

Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations