REST API Fuzzing Using API Dependencies and Large Language Models

With the widespread adoption of cloud services, ensuring the quality and security of the representational state transfer application programming interface (REST API) has become critical. Among various REST API testing techniques, fuzz testing stands out as a promising approach due to its ability to automatically generate large volumes of random or malformed inputs. To improve test coverage through fuzzing, we developed an enhanced method for generating API sequences and parameter values, building upon the widely used open-source tool RESTler. The approach extends RESTler by incorporating resource-level dependencies between APIs in addition to the existing producer–consumer relationships, enabling the construction of more valid API sequences. It also leverages a large language model to automatically generate parameter values. To further ensure input validity, a feedback loop is introduced to refine invalid inputs using error messages from API responses. Experimental results show that, compared to RESTler, the proposed method increases API coverage and detects more faults on average, demonstrating its effectiveness.