Willem Pienaar and Shahram Anver discuss the growing concern over prompt injection (PI) attacks on applications built using Language Learning Models (LLMs), highlighting how such attacks can manipulate outputs, expose sensitive data, and enable unauthorized actions. Rebuff, an open-source framework, offers a solution by providing a self-hardening detection mechanism against these attacks, utilizing multiple defense layers such as heuristics, LLM-based detection, vector databases, and canary tokens. The authors demonstrate how Rebuff can be integrated into applications, showing its ability to detect potential SQL injection attacks through an example scenario. Despite its efficacy, Rebuff is still in its alpha stage and comes with limitations, including the potential for false positives and negatives and the need for ongoing development and community involvement to enhance its robustness against skilled attackers.