| Title | A Framework for Automated API Fuzzing at Enterprise Scale |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Mahmood, Riyadh, Pennington, Jay, Tsang, Danny, Tran, Tan, Bogle, Andrea |
| Conference Name | 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) |
| Keywords | API Testing, cloud computing, Cluster computing, compositionality, Conferences, Costs, Fuzz Testing, fuzzing, GraphQL Testing, intellectual property, Metrics, OpenAPI Testing, pubcrawl, resilience, Resiliency, Scalability, scalable verification, SOAP Testing, Testing-as-a-Service |
| Abstract | Web-based Application Programming Interfaces (APIs) are often described using SOAP, OpenAPI, and GraphQL specifications. These specifications provide a consistent way to define web services and enable automated fuzz testing. As such, many fuzzers take advantage of these specifications. However, in an enterprise setting, the tools are usually installed and scaled by individual teams, leading to duplication of efforts. There is a need for an enterprise-wide fuzz testing solution to provide shared, cost efficient, off-nominal testing at scale where fuzzers can be plugged-in as needed. Internet cloud-based fuzz testing-as-a-service solutions mitigate scalability concerns but are not always feasible as they require artifacts to be uploaded to external infrastructure. Typically, corporate policies prevent sharing artifacts with third parties due to cost, intellectual property, and security concerns. We utilize API specifications and combine them with cluster computing elasticity to build an automated, scalable framework that can fuzz multiple apps at once and retain the trust boundary of the enterprise. |
| Notes | ISSN: 2159-4848 |
| DOI | 10.1109/ICST53961.2022.00018 |
| Citation Key | mahmood_framework_2022 |
A Framework for Automated API Fuzzing at Enterprise Scale