Enhancing Security Using Legality Assertions
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
Buffer overflows have been the most common form of security vulnerability in the past decade. A number of techniques have been proposed to address such attacks. Some are limited to protecting the return address on the stack; others are more general, but have undesirable properties such as large overhead and false warnings. The approach described in this paper uses legality assertions, source code assertions inserted before each subscript and pointer dereference that explicitly check that the referencing expression actually specifies a location within the array or object pointed at run time. A transformation system is developed to analyze a program and annotate it with appropriate assertions automatically. This approach detects buffer vulnerabilities in both stack and heap memory as well as potential buffer overflows in library functions. Runtime checking through using automatically inferred assertions considerably enhances the accuracy and efficiency of buffer overflow detection. A number of example buffer overflow-exploiting C programs are used to demonstrate the effectiveness of this approach.
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 | 0.001 |
| 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