FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer
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Abstract (arXiv)
Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box RESTful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box RESTful API fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55% code coverage.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 19 Jul 2024.
Builds on
7 of 27 references.
References · 7
- 2021Automatic Web Testing using Curiosity-Driven Reinforcement Learningcited by 19
- 2020Magma: A Ground-Truth Fuzzing Benchmarkcited by 16
- 2022Morest: Model-based RESTful API Testing with Execution Feedbackcited by 11
- 2023Constrained Adversarial Learning for Automated Software Testing: a literature reviewcited by 6
- 2019QuickREST: Property-based Test Generation of OpenAPI-Described RESTful APIscited by 4
- 2021Restats: A Test Coverage Tool for RESTful APIscited by 4
- 2023TestLab: An Intelligent Automated Software Testing Frameworkcited by 1
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Citing papers per year: 2025: 2.
2 papers.
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- 2024Fixing Security Vulnerabilities with AI in OSS-Fuzz
- 2025LibLMFuzz: LLM-Augmented Fuzz Target Generation for Black-box Libraries
- 2025FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI
- 2025Orion: Fuzzing Workflow Automation
- 2025AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL
- 2025ROSA: Finding Backdoors with Fuzzing ↗
- 2026Large Language Models for Fuzz Testing in Microservices: A Systematic Literature Review ↗
- 2026Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis