AQUA: Scalable Rowhammer Mitigation by Quarantining Aggressor Rows at Runtime

Rowhammer allows an attacker to induce bit flips in a row by rapidly accessing neighboring rows. Rowhammer is a severe security threat as it can be used to escalate privilege or break confidentiality. Moreover, the threshold of activations needed to induce Rowhammer continues to reduce and new attacks like Half-Double break existing solutions that refresh victim rows. The recently proposed Randomized Row-Swap (RRS) scheme is resilient to Half-Double as it provides mitigation by swapping an aggressor row with a random row. However, to ensure security, the threshold for triggering a row-swap must be set much lower than the Rowhammer threshold, leading to a significant performance loss of 20% on average, at a Rowhammer threshold of 1K. Furthermore, the SRAM overhead for storing the indirection table of RRS becomes prohibitively large -2.4MB per rank at a Rowhammer threshold of 1K. Our goal is to develop a scalable Rowhammer mitigation that incurs negligible performance and storage overheads.

To this end, we propose AQUA, a Rowhammer mitigation that breaks the spatial correlation between aggressor and victim rows by dynamically quarantining the aggressor row in a dedicated region of memory. AQUA allows for an effective row migration threshold much higher than in RRS, leading to an order of magnitude less slowdown and SRAM. As the security of AQUA is not reliant on keeping the destination row a secret, we further reduce the SRAM overheads of the indirection table by storing it in DRAM, and accessing it on-demand. We derive the size of the quarantine region required to ensure security for AQUA and show that reserving about 1% of DRAM is sufficient to mitigate Rowhammer at a threshold of 1K. Our evaluations show that AQUA incurs an average slowdown of 2% and an SRAM overhead (for mapping and migration) of only 41KB per rank at a Rowhammer threshold of 1K.