Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The number of cybersecurity vulnerabilities keeps growing every year. Each vulnerability must be reported to the MITRE Corporation and assessed by a Counting Number Authority, which generates a metrics vector that determines its severity score. This process can take up to several weeks, with higher-severity vulnerabilities taking more time. Several authors have successfully used Deep Learning to automate the score generation process and used explainable AI to build trust with the users. However, the explanations that were shown were surface label input saliency on binary classification. This is a limitation, as several metrics are multi-class and there is much more we can achieve with XAI than just visualizing saliency. In this work, we look for actionable actions CNAs can take using XAI. We achieve state-of-the-art results using an interpretable XGBoost model, generate explanations for multi-class labels using SHAP, and use the raw Shapley values to calculate cumulative word importance and generate IF rules that allow a more transparent look at how the model classified vulnerabilities. Finally, we made the code and dataset open-source for reproducibility.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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 it