{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deriving-semantics-aware-fuzzers-from-web-api","title":"Deriving Semantics-Aware Fuzzers from Web API Schemas","arxiv_id":"2112.10328","date":"2021-12-20","proceeding":null,"authors":["Zac Hatfield-Dodds","Dmitry Dygalo"],"abstract":"Fuzzing -- whether generating or mutating inputs -- has found many bugs and security vulnerabilities in a wide range of domains. Stateful and highly structured web APIs present significant challenges to traditional fuzzing techniques, as execution feedback is usually limited to a response code instead of code coverage and vulnerabilities of interest include silent information-disclosure in addition to explicit errors. Our tool, Schemathesis, derives structure- and semantics-aware fuzzers from web API schemas in the OpenAPI or GraphQL formats, using property-based testing tools. Derived fuzzers can be incorporated into unit-test suites or run directly, with or without end-user customisation of data generation and semantic checks. We construct the most comprehensive evaluation of web API fuzzers to date, running eight fuzzers against sixteen real-world open source web services. OpenAPI schemas found in the wild have a long tail of rare features and complex structures. Of the tools we evaluated, Schemathesis was the only one to handle more than two-thirds of our target services without a fatal internal error. Schemathesis finds 1.4 times to 4.5 times more unique defects than the respectively second-best fuzzer for each target, and is the only fuzzer to find defects in four targets.","url_abs":"https://arxiv.org/abs/2112.10328v1","url_pdf":"https://arxiv.org/pdf/2112.10328v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deriving-semantics-aware-fuzzers-from-web-api","repo_url":"https://github.com/schemathesis/web-api-fuzzing-project","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deriving-semantics-aware-fuzzers-from-web-api","repo_url":"https://github.com/schemathesis/schemathesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}