Managing Publicly Known Security Vulnerabilities in Software Systems
Bibliographic record
Abstract
Monitoring security vulnerabilities (weaknesses in software systems) is very important for organizations. Third parties such as National Institute of Standards and Technology (NIST) regularly publish vulnerability reports to secure national networks and protect business interests. The main challenge in this context is that the software systems against which the vulnerabilities are published are typically known differently to various stake holders that consume those vulnerable software systems. For instance, an organization may refer to one of its software components as my.program.js, however NIST may report a vulnerability on that particular software component as $org\lrcorner Jrogram\lrcorner S$ according to their standards. Thousands of vulnerabilities are reported against millions of software compo- nents every year, which makes this problem very complex. In this paper, we propose a system that matches imprecise pieces of data to track vulnerabilities in software systems. The heart of the proposed system is a text mining technique that is capable of searching vulnerabilities from large volumes of data regardless of how the software systems are named. Our extensive experiments with real datasets reveal that the proposed system is capable of capturing vulnerabilities with more than 90% accuracy.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".