ASTRA learns constraints from 4xx response messages using LLMs, then regenerates API test cases, cutting invalid requests and achieving high coverage with far fewer API calls than prior tools.
REST API Fuzzing by Coverage Level Guided Blackbox Testing
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abstract
With the growth of web applications, REST APIs have become the primary communication method between services. In order to ensure system reliability and security, software quality can be assured by effective testing methods. Black box fuzz testing is one of the effective methods to perform tests on a large scale. However, conventional black box fuzz testing generates random data without judging the quality of the input. We implement a black box fuzz testing method for REST APIs. It resolves the issues of blind mutations without knowing the effectiveness by Test Coverage Level feedback. We also enhance the mutation strategies by reducing the testing complexity for REST APIs, generating more appropriate test cases to cover possible paths. We evaluate our method by testing two large open-source projects and 89 bugs are reported and confirmed. In addition, we find 351 bugs from 64 remote API services in APIs.guru. The work is in https://github.com/iasthc/hsuan-fuzz.
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Utilizing API Response for Test Refinement
ASTRA learns constraints from 4xx response messages using LLMs, then regenerates API test cases, cutting invalid requests and achieving high coverage with far fewer API calls than prior tools.