PrIDE: Achieving Secure Rowhammer Mitigation with Low-Cost In-DRAM Trackers
Bibliographic record
Abstract
Rowhammer-induced bit-flips are a threat to DRAM security. To mitigate Rowhammer, DDR4 devices employ TRR, an in-DRAM tracker, to identify aggressor rows. In-DRAM trackers tend to be severely resource-constrained (1-30 entries), which means they cannot reliably track all the aggressor rows and are bound to fail for some access patterns. Unfortunately, for existing in-DRAM trackers, it is difficult to a priori determine how often they will fail when subjected to the worst-case pattern. Unsurprisingly, all the current low-cost in-DRAM trackers have been broken with specific access patterns within a few minutes. While provably secure alternatives for in-DRAM tracking exist, they require thousands of tracking entries, making them unappealing for commercial adoption. The goal of our paper is to develop a low-cost in-DRAM tracker that is secure (guarantees a time-to-failure in the range of years) against all access patterns.We contend that the root cause of the vulnerability of current low-cost in-DRAM trackers stems from the use of activation-counters to direct policy decisions (e.g. which rows to insert, which to evict, and which to mitigate). Therefore, an attacker can perform frequent accesses to dummy rows to evade the mitigation of an aggressor row. The key insight of our paper is that to ensure security, the policy decisions of an in-DRAM tracker must not depend on the access pattern. To that end, we propose a secure and low-cost in-DRAM tracker called PrIDE, which consists of a FIFO buffer with probabilistic insertion. As the policy decisions of PrIDE do not depend on the access pattern, we develop a framework to calculate the time-to-failure. Our analysis with DDR5 shows that PrIDE (with 4 entries, 10byte storage) can tolerate Rowhammer thresholds of 1.9 K while guaranteeing per-bank time-to-failure of more than 10,000 years for all access patterns. We also co-design PrIDE with RFM to tolerate thresholds as low as 400 with only $1.6 \%$ slowdown. To the best of our knowledge, PrIDE is the first low-cost in-DRAM tracker to achieve provably secure Rowhammer mitigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".