{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260100773"},"data":{"type":"grant-details","id":"DE260100773","attributes":{"code":"DE260100773","administering-organisation":"Adelaide University","announcement-administering-organisation":"The University of Adelaide","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Efficient Bug Detection for Reliable AI Software Infrastructures. AI software infrastructures, such as PyTorch and TensorRT, form the backbone of AI technologies and are increasingly critical for the widespread adoption of AI. However, bugs within these software systems can cause AI applications to make wrong decisions, leading to catastrophic failures such as car crashes. This project aims to enhance the reliability and cybersecurity of these systems by developing bug-directed fuzzing techniques for efficient bug detection. The expected outcomes include cutting-edge fuzzing methods and a new framework for systematic testing. This project will significantly mitigate risks and enhance the profitability of AI, particularly in high-impact sectors such as smart transportation, agriculture and manufacturing.","funding-current":500543.00,"funding-at-announcement":496641,"investigators-current":[{"title":"Dr","firstName":"Xiaogang","familyName":"Zhu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0647-4747 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Xiaogang","familyName":"Zhu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0647-4747 "}],"organisations-current":[{"organisationName":"Adelaide University","roleName":"Administering Organisation","state":"SA"}],"organisations-at-announcement":[{"organisationName":"The University of Adelaide","roleName":"Administering Organisation","state":"SA"}],"field-of-research":[{"isPrimary":true,"code":"4604","name":"Cybersecurity and Privacy","type":"FOR20"},{"isPrimary":false,"code":"460407","name":"System and Network Security","type":"FOR20"},{"isPrimary":false,"code":"460502","name":"Data Mining and Knowledge Discovery","type":"FOR20"}],"socio-economic-objective":[{"code":"220403","name":"Artificial Intelligence","type":"SEO20"},{"code":"220405","name":"Cybersecurity","type":"SEO20"}],"international-collaboration":[],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"Artificial Intelligence (AI) technologies are now deeply integrated into modern society, transforming the way people live and work, and large language models have gained significant attention, driving further the adoption of AI. At the core of these technologies are AI software infrastructures, which are the essential software systems that support AI development, model deployment across machines, and performance optimisation. However, the reliability of AI software infrastructures remains at risk due to insufficient testing. Failures in these software systems can lead to severe cybersecurity consequences when deploying AI solutions. Addressing these bugs is vital for economic stability, with Australia's cybersecurity industry protecting critical systems and adding over $2 billion annually. This project aims to develop advanced testing techniques to thoroughly and systematically evaluate AI software infrastructures, ensuring their reliability and secure performance. The outcomes of this project will benefit users both nationally and internationally, especially in critical sectors such as smart transportation, smart agriculture, and digital manufacturing, where reliable AI software infrastructures can reduce risks such as car crashes, compromising food security, and misoperation of machines when using AI. Results will be disseminated through public engagement activities via workshops, seminars, public talks, top-tier publications, and industry collaborations."}}}