REST-ler: Automatic Intelligent REST API Fuzzing
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Cloud services have recently exploded with the advent of powerful cloud-computing platforms such as Amazon Web Services and Microsoft Azure. Today, most cloud services are accessed through REST APIs, and Swagger is arguably the most popular interface-description language for REST APIs. A Swagger specification describes how to access a cloud service through its REST API (e.g., what requests the service can handle and what responses may be expected). This paper introduces REST-ler, the first automatic intelligent REST API security-testing tool. REST-ler analyzes a Swagger specification and generates tests that exercise the corresponding cloud service through its REST API. Each test is defined as a sequence of requests and responses. REST-ler generates tests intelligently by (1) inferring dependencies among request types declared in the Swagger specification (e.g., inferring that "a request B should not be executed before a request A" because B takes as an input argument a resource-id x returned by A) and by (2) analyzing dynamic feedback from responses observed during prior test executions in order to generate new tests (e.g., learning that "a request C after a request sequence A;B is refused by the service" and therefore avoiding this combination in the future). We show that these two techniques are necessary to thoroughly exercise a service under test while pruning the large search space of possible request sequences. We also discuss the application of REST-ler to test GitLab, a large popular open-source self-hosted Git service, and the new bugs that were found.
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