Toward preventing stack overflow using kernel properties
Bibliographic record
Abstract
We contribute to the investigation of buffer overflows by finding a more accurate way of preventing their exploitation. We work at the highest privilege levels and in the safest part of a GNU/Linux system, namely the kernel. We provide a system that allows the kernel to detect overflows and prevent their exploitation. The kernel injects at launch time some (minimal) code into the binary being run, and subsequently uses this code to monitor the execution of that program with respect to its stack use, thus detecting stack overflows. The system stands alone in the sense that it does not need any hardware support; it also works on any program, no matter how that program was conceived or compiled. Beside the theoretical concepts we also present a proof-of-concept patch to the kernel supporting our idea. Overall we effectively show that guarding against buffer overflows at run time is not only possible but also feasible. In addition we take the first steps toward implementing such a defense.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".