Intelligent REST API data fuzzing

The cloud runs on REST APIs. In this paper, we study how to intelligently generate data payloads embedded in REST API requests in order to find data-processing bugs in cloud services. We discuss how to leverage REST API specifications, which, by definition, contain data schemas for API request bodies. We then propose and evaluate a range of data fuzzing techniques, including structural schema fuzzing rules, various rule combinations, search heuristics, extracting data values from examples included in REST API specifications, and learning data values on-the-fly from previous service responses. After evaluating these techniques, we identify the top-performing combination and use this algorithm to fuzz several Microsoft Azure cloud services. During our experiments, we found 100s of “Internal Server Error” service crashes, which we triaged into 17 unique bugs and reported to Azure developers. All these bugs are reproducible, confirmed, and fixed or in the process of being fixed.

The cloud runs on REST APIs. In this paper, we study how to intelligently generate data payloads embedded in REST API requests in order to find data-processing bugs in cloud services. We discuss how to leverage REST API specifications, which, by definition, contain data schemas for API request bodies. We then propose and evaluate a range of data fuzzing techniques, including structural schema fuzzing rules, various rule combinations, search heuristics, extracting data values from examples included in REST API specifications, and learning data values on-the-fly from previous service responses. After evaluating these techniques, we identify the top-performing combination and use this algorithm to fuzz several Microsoft Azure cloud services. During our experiments, we found 100s of “Internal Server Error” service crashes, which we triaged into 17 unique bugs and reported to Azure developers. All these bugs are reproducible, confirmed, and fixed or in the process of being fixed.