PT-Guard: Integrity-Protected Page Tables to Defend Against Breakthrough Rowhammer Attacks
Bibliographic record
Abstract
Page tables enforce process isolation in systems. Rowhammer attacks break process isolation by flipping bits in DRAM to tamper page tables and achieving privilege escalation. Moreover, new Rowhammer attacks break existing mitigations. We seek to protect systems against such breakthrough attacks. We present PT-Guard, an integrity protection mechanism for page tables. PT-Guard uses unused bits in Page Table Entries (PTE) to embed a Message Authentication Code (MAC) for the PTE cacheline without any storage overhead. These unused bits arise from PTEs supporting petabytes of physical memory while systems targeted by Rowhammer use at-most terabytes of mem-ory. By storing and verifying MACs for PTEs, PT-Guard detects arbitrary bit-flips in PTEs. Moreover, PT-Guard also provides best-effort correction of faulty-PTEs leveraging value locality. PT-Guard protects page tables from breakthrough Rowhammer attacks with negligible hardware changes, no DRAM storage, <72 bytes of SRAM, 1.3% slowdown, and no software changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".