Repairing return address stack for buffer overflow protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although many defense mechanisms against buffer overflow attacks have been proposed, buffer overflow vulnerability in software is still one of the most prevalent vulnerabilities exploited. This paper proposes a micro-architecture based defense mechanism against buffer overflow attacks. As buffer overflow attack leads to a compromised return address, our approach is to provide a software transparent micro-architectural support for return address integrity checking. By keeping an uncompromised copy of the return address separate from the activation record in run-time stack, the return address compromised by a buffer overflow attack can be detected at run time. Since extra copies of return addresses are already found in the return address stack (RAS) for return address prediction in most high-performance microprocessors, this paper considers augmenting the RAS in speculative superscalar processors for return address integrity checking. The new mechanism provides 100% accurate return address prediction as well as integrity checking for return addresses. Hence, it enhances system performance in addition to preventing a buffer overflow attack.
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.
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 | 0.000 |
| Open science | 0.000 | 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 it