Tao Zheng1, Rui Tang1,2,3, Xingshu Chen1,2,3,*, Changxiang Shen1
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1595-1612, 2024, DOI:10.32604/cmc.2024.055180
- 15 October 2024
Abstract RESTful API fuzzing is a promising method for automated vulnerability detection in Kubernetes platforms. Existing tools struggle with generating lengthy, high-semantic request sequences that can pass Kubernetes API gateway checks. To address this, we propose KubeFuzzer, a black-box fuzzing tool designed for Kubernetes RESTful APIs. KubeFuzzer utilizes Natural Language Processing (NLP) to extract and integrate semantic information from API specifications and response messages, guiding the generation of more effective request sequences. Our evaluation of KubeFuzzer on various Kubernetes clusters shows that it improves code coverage by 7.86% to 36.34%, increases the successful response rate by More >
Login
Register
