QPRAC: Towards Secure and Practical PRAC-based Rowhammer Mitigation using Priority Queues

TL;DR

This paper introduces QPRAC, a secure, scalable, and practical Rowhammer mitigation method using priority queues within the PRAC framework, achieving low performance impact and effective protection at low thresholds.

Contribution

It presents the first secure and practical PRAC-based Rowhammer mitigation using a priority queue design and combined mitigation strategies.

Findings

Mitigates Rowhammer at thresholds as low as 71.

Achieves only 0.8% slowdown for benign workloads.

Reduces to 0% slowdown with proactive mitigation.

Abstract

JEDEC has introduced the Per Row Activation Counting (PRAC) framework for DDR5 and future DRAMs to enable precise counting of DRAM row activations. PRAC enables a holistic mitigation of Rowhammer attacks even at ultra-low Rowhammer thresholds. PRAC uses an Alert Back-Off (ABO) protocol to request the memory controller to issue Rowhammer mitigation requests. However, recent PRAC implementations are either insecure or impractical. For example, Panopticon, the inspiration for PRAC, is rendered insecure if implemented per JEDEC's PRAC specification. On the other hand, the recent UPRAC proposal is impractical since it needs oracular knowledge of the `top-N' activated DRAM rows that require mitigation. This paper provides the first secure, scalable, and practical RowHammer solution using the PRAC framework. The crux of our proposal is the design of a priority-based service queue (PSQ) for mitigations that prioritizes pending mitigations based on activation counts to avoid the security risks of prior solutions. This provides principled security using the reactive ABO protocol. Furthermore, we co-design our PSQ, with opportunistic mitigation on Refresh Management (RFM) operations and proactive mitigation during refresh (REF), to limit the performance impact of ABO-based mitigations. QPRAC provides secure and practical RowHammer mitigation that scales to Rowhammer thresholds as low as 71 while incurring a 0.8% slowdown for benign workloads, which further reduces to 0% with proactive mitigations.