Challenges and Design Issues in Finding CUDA Bugs via GPU-Native Fuzzing

[2603.05725] Challenges and Design Considerations for Finding CUDA Bugs Through GPU-Native Fuzzing --> Computer Science > Cryptography and Security arXiv:2603.05725 (cs) [Submitted on 5 Mar 2026] Title: Challenges and Design Considerations for Finding CUDA Bugs Through GPU-Native Fuzzing Authors: Mingkai Li , Joseph Devietti , Suman Jana , Tanvir Ahmed Khan View a PDF of the paper titled Challenges and Design Considerations for Finding CUDA Bugs Through GPU-Native Fuzzing, by Mingkai Li and 3 other authors View PDF HTML (experimental) Abstract: Modern computing is shifting from homogeneous CPU-centric systems to heterogeneous systems with closely integrated CPUs and GPUs. While the CPU software stack has benefited from decades of memory safety hardening, the GPU software stack remains dangerously immature. This discrepancy presents a critical ethical challenge: the world's most advanced AI and scientific workloads are increasingly deployed on vulnerable hardware components. In this paper, we study the key challenges of ensuring memory safety on heterogeneous systems. We show that, while the number of exploitable bugs in heterogeneous systems rises every year, current mitigation methods often rely on unfaithful translations, i.e., converting GPU programs to run on CPUs for testing, which fails to capture the architectural differences between CPUs and GPUs. We argue that the faithfulness of the program behavior is at the core of secure and reliable heterogeneous systems design. To ensure faithfulness, we discuss several design considerations of a GPU-native fuzzing pipeline for CUDA programs. Comments: Accepted to appear in HotEthics 2026; 6 pages, 1 figure Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.05725 [cs.CR] (or arXiv:2603.05725v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2603.05725 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mingkai Li [ view email ] [v1] Thu, 5 Mar 2026 22:26:36 UTC (140 KB) Full-text links: Access Paper: View a PDF of the paper titled Challenges and Design Considerations for Finding CUDA Bugs Through GPU-Native Fuzzing, by Mingkai Li and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )