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Open Problems in Fuzzing RESTful APIs: A Comparison of Tools

Authors :
Zhang, Man
Arcuri, Andrea
Zhang, Man
Arcuri, Andrea
Publication Year :
2022

Abstract

RESTful APIs are a type of web services that are widely used in industry. In the last few years, a lot of effort in the research community has been spent in designing novel techniques to automatically fuzz those APIs to find faults in them. Many real faults were automatically found in a large variety of RESTful APIs. However, usually the analyzed fuzzers treat the APIs as black-box, and no analysis of what is actually covered in these systems is done. Therefore, although these fuzzers are clearly useful for practitioners, we do not know what are their current limitations and actual effectiveness. Solving this is a necessary step to be able to design better, more efficient and effective techniques. To address this issue, in this paper we compare seven state-of-the-art fuzzers on 18 open-source and one industrial RESTful APIs. We then analyzed the source code of which parts of these APIs the fuzzers fail to generate tests for. This analysis points to clear limitations of these current fuzzers, listing concrete challenges for the research community to follow up on.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1333770049
Document Type :
Electronic Resource