GuaNary: Efficient Buffer Overflow Detection In Virtualized Clouds Using Intel EPT-based Sub-Page Write Protection Support
Bibliographic record
Abstract
Write buffer overflow is a widespread and prevalent memory safety violation in C/C++, reported as the top vulnerability in 2022 and 2023. Secure memory allocators are generally used to protect systems against attacks that may exploit buffer overflows. Existing allocators mainly rely on two types of countermeasures to prevent or detect write overflows: canaries and guard pages, each with pros and cons in terms of detection latency and memory footprint. For virtualized cloud applications, this paper follows the Out of Hypervisor (OoH) trend and introduces GuaNary, a safety guard against write overflows, allowing synchronous detection at a low memory footprint cost. OoH is a new virtualization research axis introduced in 2022 advocating the exposure of hardware features for virtualization to the guest OS so that its processes can take advantage of them. Based on the OoH principle, GuaNary leverages Intel Sub-Page write Permission (SPP), a recent hardware virtualization feature that allows to write-protect guest memory at the granularity of 128B (namely, sub-page) instead of 4KB. We implement a software stack, LeanGuard, which promotes the utilization of SPP from inside virtual machines by new secure allocators that use GuaNary. Our evaluation shows that for the same number of protected buffers, LeanGuard consumes 8.3× less memory than SlimGuard, a recent state-of-art secure allocator. Further, for the same memory consumption, LeanGuard allows protecting 25× more buffers than SlimGuard.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".