Mining Five Years of Actively Exploited Vulnerabilities
Bibliographic record
Abstract
Companies and researchers rely heavily on the National Vulnerability Database (NVD) in order to be cognizant of the threat environment they face. However, research shows that only about 5% of reported vulnerabilities are eventually exploited. Our study compares exploited and non-exploited vulnerabilities to provide valuable insights for advancing research on effective vulnerability prioritization. To achieve this, we compile a database of over 4,000 exploited vulnerabilities, spanning 5 years, from 2019 to 2023. We further mine this dataset to uncover trends and patterns that relate to how exploited vulnerabilities differ from non-exploited ones and how exploited vulnerabilities evolved over the 5-year spans of our study. We found that exploited vulnerabilities differ from non-exploited vulnerabilities with respect to combinations of CVSS score attributes, but not with respect to the attributed CVSS score when considered in isolation. We further found that the CVSS scores of exploited vulnerabilities were largely stable over time for the duration of the study, and that widely used security resources are not concordant with the data we observed.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".