START: Scalable Tracking for any Rowhammer Threshold
Anish Saxena, Moinuddin K. Qureshi
摘要
The Rowhammer vulnerability is worsening, with the Rowhammer Threshold (T RH ) reducing from 139K to 4.8K activations over the last decade. As thresholds reduce further, the number of possible aggressor rows increases inversely, making it difficult to reliably track such rows in a storage-efficient manner for typical Rowhammer defenses. To be secure at lower thresholds, academic trackers like Graphene must dedicate prohibitively high storage (hundreds of KBs to MBs) at the chip's design time. Recent in-DRAM trackers from the industry, such as DSAC-TRR, perform approximate tracking and sacrifice guaranteed protection for reduced storage overheads, leaving DRAM vulnerable to Rowhammer attacks. Ideally, we seek a configurable tracker that is secure and precise, incurs negligible dedicated storage and performance overheads, and scales at deployment to track arbitrarily low thresholds.
To that end, we propose START -a Scalable Tracker for Any Rowhammer Threshold. Rather than relying on dedicated SRAM structures, START dynamically repurposes a small fraction of the Last-Level Cache (LLC) to store tracking metadata. START leverages the observation that while the memory contains millions of rows, typical workloads touch only a small subset of rows within a refresh period of 64ms. Thus, allocating tracking entries on demand reduces storage significantly. If the application does not access many rows in memory, START does not reserve any LLC capacity. Otherwise, START dynamically uses 1-way, 2-way, or 8-way of the cache set based on demand. START consumes, on average, 9.4% of the LLC capacity to store metadata, which is 5× lower compared to dedicating a counter in LLC for each row in memory. We also propose START-M, a memory-mapped START for large-memory systems. Our designs require only 4KB SRAM for newly added structures and perform within 1% of idealized tracking even at T RH of less than 100.
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引用它的顶会 Paper15
- QPRAC: Towards Secure and Practical PRAC-based Rowhammer Mitigation using Priority QueuesJeonghyun Woo, Shaopeng Chris Lin, Prashant J. Nair, Aamer Jaleel 等HPCA 2025 · 被引用 20 次
- MOAT: Securely Mitigating Rowhammer with Per-Row Activation CountersMoinuddin Qureshi, Salman QaziASPLOS 2025 · 被引用 19 次
- Variable Read Disturbance: An Experimental Analysis of Temporal Variation in DRAM Read DisturbanceAtaberk Olgun, F. Nisa Bostanci, Ismail Emir Yüksel, Oguzhan Canpolat 等HPCA 2025 · 被引用 15 次
- ImPress: Securing DRAM Against Data-Disturbance Errors via Implicit Row-Press MitigationAnish Saxena, Aamer Jaleel, Moinuddin QureshiMICRO 2024 · 被引用 15 次
- AutoRFM: Scaling Low-Cost in-DRAM Trackers to Ultra-Low Rowhammer ThresholdsMoinuddin QureshiHPCA 2025 · 被引用 15 次
它引用的顶会 Paper18
- Drammer: Deterministic Rowhammer Attacks on Mobile PlatformsVictor van der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss 等CCS 2016 · 被引用 381 次
- Another Flip in the Wall of Rowhammer DefensesDaniel Gruss, Moritz Lipp, Michael Schwarz, Daniel Genkin 等S&P 2018 · 被引用 288 次
- TRRespass: Exploiting the Many Sides of Target Row RefreshPietro Frigo, Emanuele Vannacci, Hasan Hassan, Victor van der Veen 等S&P 2020 · 被引用 274 次
- RAMBleed: Reading Bits in Memory Without Accessing ThemAndrew Kwong, Daniel Genkin, Daniel Gruss, Yuval YaromS&P 2020 · 被引用 239 次
- Exploiting Correcting Codes: On the Effectiveness of ECC Memory Against Rowhammer AttacksLucian Cojocar, Kaveh Razavi, Cristiano Giuffrida, Herbert BosS&P 2019 · 被引用 233 次
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