SV-AF — A Security Vulnerability Analysis Framework
Bibliographic record
Abstract
The globalization of the software industry has introduced a widespread use of system components across traditional system boundaries. Due to this global reuse, also vulnerabilities and security concerns are no longer limited in their scope to individual systems but instead can now affect global software ecosystems. While known vulnerabilities and security concerns are reported in specialized vulnerability databases, these repositories often remain information silos. In this research, we introduce a modeling approach, which eliminates these silos by linking security knowledge with other software artifacts to improve traceability and trust in software products. In our approach, we introduce a Security Vulnerabilities Analysis Framework (SV-AF) to support evidence based vulnerability detection. Two case studies are presented to illustrate the applicability of our presented approach. In these case studies, we link the NVD vulnerability databases and the Maven build repository to trace vulnerabilities across repository and project boundaries. In our analysis, we identify that 750 Maven project releases are directly affected by known security vulnerabilities and by considering transitive dependencies, an additional 415604 Maven projects can be identified as potentially affected by these vulnerabilities.
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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".