Machine learning (ML) and artificial intelligence (AI) technologies are being adopted for a wide range of applications. This has spurred interest in the use of ML and AI for chip design. Chip design is currently heavily automated using electronic design automation (EDA) software. Recent work has shown that AI and ML methods can further increase the level of automation as well as improve the quality of existing EDA tools. However, ML methods pose their own dangers and risks. They have been shown to be easily tricked by small changes in their inputs. They can also be easily "backdoored" by modifying only a tiny fraction of their training data. While these risks have been extensively studied in other domains, their impact has not been extensively examined in AI/ML based EDA and chip design tools. This project's novelties are (1) the first comprehensive look at the impact of input and training data perturbations and attacks on the quality, performance and security of AI/ML based EDA tools; and (2) the first thorough investigation into mechanisms to defend against such attacks. The project's broader significance and importance is that enables the trustworthy adoption of AI/ML methods in the chip design industry, resulting in greatly enhanced productivity and chip design quality, while ensuring trustworthiness. The project pursues these aims in three research thrusts. Thrust 1 focuses on discovering meaningful and contextual perturbations of inputs to the different steps in th