ASSESSING TEST SUITES FOR BUFFER OVERFLOW VULNERABILITIES
Bibliographic record
Abstract
Over the last few years, numerous vulnerabilities have been reported in software, and successful exploitations of these vulnerabilities have resulted in severe consequences such as denial of services and application state corruptions. Researches have shown that effective quality assurance methods can prevent such consequences when applied during software (or applications) development processes. Software security testing is a popular assurance method in this direction. However, effective testing involves obtaining an effective test suite (or collection of test cases) that can reveal specific faults. Over the last few years, different testing approaches have been applied for revealing vulnerabilities in software. However, only few works have assessed the effectiveness of test suites for revealing vulnerabilities. We believe that bringing the idea of mutation-based assessment of test adequacy for vulnerabilities can help in detecting and removing vulnerabilities proactively. In this work, we apply mutation-based adequate testing for one of the worst vulnerabilities namely buffer overflow (BOF). We propose 16 mutation operators to force the generation of adequate test suites for BOF vulnerabilities. A prototype tool is developed to automatically generate mutants and perform mutation analysis with input test cases. The effectiveness of the operators is evaluated by using several benchmark programs having BOF vulnerabilities, and the results indicate that the proposed operators are effective for testing BOF vulnerabilities. Moreover, we present an analysis to find selective mutation operators for reducing the cost of mutation-based testing of BOF vulnerabilities.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".