This talk details the use of MWR's platform agnostic kernel fuzzing techniques to automatically identify critical flaws within Apple macOS. This talk will focus on how the researchers approached developing fuzzing automation to test the core subsystems of the XNU kernel and the insights gained, and also highlight architectural differences between other supported platforms which had to be addressed during this work. The old adage of ‘different fuzzers find different bugs' will also be explored, as we looked into the effectiveness of using targeted fuzzing for specific components considered most likely to yield vulnerabilities. An in-memory fuzzer based on a combination of static and dynamic analysis was also constructed to target these components with the aim to achieve greater code coverage, efficiency and to allow attacks on other privileged components within macOS via IPC. Finally we will discuss the issues discovered by the fuzzers and highlight future improvements which could be made to the tooling going forward to increase coverage and effectiveness. Various tools used during the research will be released after the talk.