Over the recent years, the software industry has faced a steady increase in the number of exposed and exploited software vulnerabilities. With more software and devices being connected to the internet every day, the need for proactive security measures has never been more important. One promising new technology for making software more secure is fuzz testing. This automated testing technique is based around generating a large number of test cases with the intention of revealing dangerous bugs and vulnerabilities. In this thesis work, a new direction within grey-box fuzz testing is evaluated against previous work. The presented approach uses sampled probability data in order to guide the fuzz testing towards program states that are expected to be easy to reach and beneficial for the discovery of software vulnerabilities. Evaluation of the design shows that the suggested approach provides no obvious advantage over existing solutions, but also indicates that the performance advantage could be dependent on the structure of the system under test. However, analysis of the design itself highlights several design decisions that could benefit from more extensive research. While the design proposed in this thesis work is insufficient for replacing current state of the art fuzz testing software, it provides a solid foundation for future research within the field. With the many insights gained from the design and implementation work, this thesis work aims to both inspire others and showcase the challenges of creating a probability-based approach to grey-box fuzz testing.