Classification of Static Analysis-Based Buffer Overflow Detectors
Bibliographic record
Abstract
Buffer overflow is one of the most dangerous exploitable vulnerabilities in released software or programs. Many approaches are applied to mitigate buffer overflow (BOF) vulnerabilities such as testing and monitoring. However, BOF vulnerabilities are discovered in programs frequently which might be exploited to crash programs and execute arbitrary injected code. Static analysis is a popular approach for detecting BOF vulnerabilities before releasing programs. Many static analysis-based approaches are currently used in practice. However, there is no detailed classification of these approaches to understand their common characteristics, objectives, and limitations. In this paper, we classify static analysis-based BOF vulnerability detection approaches based on six features: inference technique, analysis sensitivity, analysis granularity, soundness, completeness, and language. We then classify static inference techniques into four types: tainted data flow, constraint, annotation, and string pattern matching. Moreover, we compare the approaches in terms of effectiveness, scalability, and required manual effort. The classification will enable researchers to differentiate among existing analysis approaches. We develop some guidelines to help in choosing approaches and building tools suitable for practitioners need.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".