{"id":828229,"date":"2022-03-18T03:03:39","date_gmt":"2022-03-18T10:03:39","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=828229"},"modified":"2024-09-26T01:20:29","modified_gmt":"2024-09-26T08:20:29","slug":"revizor-testing-black-box-cpus-against-speculation-contracts","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/revizor-testing-black-box-cpus-against-speculation-contracts\/","title":{"rendered":"Revizor: Testing Black-box CPUs against Speculation Contracts"},"content":{"rendered":"

Speculative vulnerabilities such as Spectre and Meltdown expose speculative execution state that can be exploited to leak information across security domains via side-channels. Such vulnerabilities often stay undetected for a long time as we lack the tools for systematic testing of CPUs to find them.<\/p>\n

In this paper, we propose an approach to automatically detect microarchitectural information leakage in commercial black-box CPUs. We build on speculation contracts, which we employ to specify the permitted side effects of program execution on the CPU’s microarchitectural state. We propose a Model-based Relational Testing (MRT) technique to empirically assess the CPU compliance with these specifications.<\/p>\n

We implement MRT in a testing framework called Revizor, and showcase its effectiveness on real Intel x86 CPUs. Revizor automatically detects violations of a rich set of contracts, or indicates their absence. A highlight of our findings is that Revizor managed to automatically surface Spectre, MDS, and LVI, as well as several previously unknown variants.<\/p>\n","protected":false},"excerpt":{"rendered":"

Speculative vulnerabilities such as Spectre and Meltdown expose speculative execution state that can be exploited to leak information across security domains via side-channels. Such vulnerabilities often stay undetected for a long time as we lack the tools for systematic testing of CPUs to find them. In this paper, we propose an approach to automatically detect 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MICRO Top Pick","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/03\/Revizor.pdf","id":"828235","title":"revizor","label_id":"243103","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":828235,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/03\/Revizor.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Oleksii Oleksenko","user_id":43146,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Oleksii Oleksenko"},{"type":"text","value":"Christof Fetzer","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Boris 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