During December 2024, Tiger Du focused on improving the reliability of gradient-based computations in the Azure/PyRIT repository. He addressed a critical bug in the GCG Attack module by standardizing the handling of infinity values, replacing np.infty with np.inf to ensure consistent behavior for non-ASCII token scenarios. This Python-based update enhanced numerical stability and reduced the risk of NaN or infinite values during gradient updates, directly impacting the robustness of machine learning attack and defense simulations. Tiger applied his expertise in cybersecurity and machine learning to deliver a targeted fix, demonstrating careful attention to detail and a methodical engineering approach.