A dynamic detection method against ROP and JOP
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
As the proposition of the idea of Return-Oriented Programming (ROP), programs will face new challenges from viruses, and many of current defense measures will be ineffective. With fine granularity, covert virus features, deliberate and sophisticated construction and rare static characteristics, ROP attack can circumvent many traditional defense measures and its variant Jump-Oriented Programming (JOP) attack makes lots of current special ROP defense tools lose their effects. Under this circumstance, it's imperative to discover the dynamic features of ROP exploits. At this time, bringing in the technology of Dynamic Binary Instrumentation (DBI) provides powerful support for dynamic analysis of ROP attack. In this paper, we will introduce a defense measure to ROP attack. By identifying malicious program execution flow and restricting the function call specification of general program libraries, we will prevent the turning-complete features of ROP attack. Our detection method can restrain malicious use of shared libraries by ROP and defend a large part of ROP attacks.
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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