{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sage-software-based-attestation-for-gpu","title":"SAGE: Software-based Attestation for GPU Execution","arxiv_id":"2209.03125","date":"2022-09-07","proceeding":null,"authors":["Andrei Ivanov","Benjamin Rothenberger","Arnaud Dethise","Marco Canini","Torsten Hoefler","Adrian Perrig"],"abstract":"With the application of machine learning to security-critical and sensitive domains, there is a growing need for integrity and privacy in computation using accelerators, such as GPUs. Unfortunately, the support for trusted execution on GPUs is currently very limited - trusted execution on accelerators is particularly challenging since the attestation mechanism should not reduce performance. Although hardware support for trusted execution on GPUs is emerging, we study purely software-based approaches for trusted GPU execution. A software-only approach offers distinct advantages: (1) complement hardware-based approaches, enhancing security especially when vulnerabilities in the hardware implementation degrade security, (2) operate on GPUs without hardware support for trusted execution, and (3) achieve security without reliance on secrets embedded in the hardware, which can be extracted as history has shown. In this work, we present SAGE, a software-based attestation mechanism for GPU execution. SAGE enables secure code execution on NVIDIA GPUs of the Ampere architecture (A100), providing properties of code integrity and secrecy, computation integrity, as well as data integrity and secrecy - all in the presence of malicious code running on the GPU and CPU. Our evaluation demonstrates that SAGE is already practical today for executing code in a trustworthy way on GPUs without specific hardware support.","url_abs":"https://arxiv.org/abs/2209.03125v1","url_pdf":"https://arxiv.org/pdf/2209.03125v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"sage-software-based-attestation-for-gpu","repo_url":"https://github.com/spcl/sage","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}