{"title":"iGPU Leak: An Information Leakage Vulnerability on Intel Integrated GPU","authors":"Wenjian He, Wei Zhang, Sharad Sinha, Sanjeev Das","doi":"10.1109/ASP-DAC47756.2020.9045745","DOIUrl":null,"url":null,"abstract":"Hardware accelerators such as integrated graphics processing units (iGPUs) are increasingly prevalent in modern systems. They typically provide multiplexing support where several user applications can share the iGPU acceleration resources. However, security in this setting has not received sufficient consideration. In this work, we disclose a critical information leakage vulnerability due to defective GPU context management. In essence, residual register values and shared local memory in the iGPU are not cleared during a context switch. As a result, adversaries can recover the secret key of a cryptographic algorithm running on an iGPU from a single snapshot of the leaking channel. User privacy is also under threat due to browser activity eavesdropping through website-fingerprinting attack with high accuracy and resolution. Moreover, this vulnerability can constitute a covert channel with a bandwidth of up to 8 Gbps.","PeriodicalId":125112,"journal":{"name":"2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASP-DAC47756.2020.9045745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Hardware accelerators such as integrated graphics processing units (iGPUs) are increasingly prevalent in modern systems. They typically provide multiplexing support where several user applications can share the iGPU acceleration resources. However, security in this setting has not received sufficient consideration. In this work, we disclose a critical information leakage vulnerability due to defective GPU context management. In essence, residual register values and shared local memory in the iGPU are not cleared during a context switch. As a result, adversaries can recover the secret key of a cryptographic algorithm running on an iGPU from a single snapshot of the leaking channel. User privacy is also under threat due to browser activity eavesdropping through website-fingerprinting attack with high accuracy and resolution. Moreover, this vulnerability can constitute a covert channel with a bandwidth of up to 8 Gbps.