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Modern systems mitigate Rowhammer using victim refresh, which refreshes thetwo neighbours of an aggressor row when it encounters a specified number ofactivations. Unfortunately, complex attack patterns like Half-Double breakvictim-refresh, rendering current systems vulnerable. Instead, recentlyproposed secure Rowhammer mitigations rely on performing mitigative action onthe aggressor rather than the victims. Such schemes employ mitigative actionssuch as row-migration or access-control and include AQUA, SRS, and Blockhammer.While these schemes incur only modest slowdowns at Rowhammer thresholds of fewthousand, they incur prohibitive slowdowns (15%-600%) for lower thresholds thatare likely in the near future. The goal of our paper is to make secureRowhammer mitigations practical at such low thresholds.  Our paper provides the key insights that benign application encounterthousands of hot rows (receiving more activations than the threshold) due tothe memory mapping, which places spatially proximate lines in the same row tomaximize row-buffer hitrate. Unfortunately, this causes row to receiveactivations for many frequently used lines. We propose Rubix, which breaks thespatial correlation in the line-to-row mapping by using an encrypted address toaccess the memory, reducing the likelihood of hot rows by 2 to 3 orders ofmagnitude. To aid row-buffer hits, Rubix randomizes a group of 1-4 lines. Wealso propose Rubix-D, which dynamically changes the line-to-row mapping.Rubix-D minimizes hot-rows and makes it much harder for an adversary to learnthe spatial neighbourhood of a row. Rubix reduces the slowdown of AQUA (from15% to 1%), SRS (from 60% to 2%), and Blockhammer (from 600% to 3%) whileincurring a storage of less than 1 Kilobyte.

Randomized Line-to-Row Mapping for Low-Overhead Rowhammer Mitigations