## 5 Application of Generative AI Models for Context-Aware Vulnerability Scoring
### Comparative Analysis of LLM Results
#### 5.4.1 Similarities  
* Class Imbalance Impact: All three models consistently demonstrated superior performance on majority classes (e.g., NETWORK Attack Vector, LOW Attack Complexity, UNCHANGED Scope, and NONE User Interaction). Conversely, they all struggled \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multirow{2}{*}{Attack Vector} & NETWORK & 98.15\% & 100.00\% & 99.07\% & 98.29\% \\  & LOCAL & 100.00\% & 81.82\% & 89.92\% & \\ \hline \multirow{2}{*}{Attack Complexity} & LOW & 93.91\% & 98.18\% & 96.00\% & 92.31\% \\  & HIGH & 0\% & 0\% & 0\% & \\ \hline \multirow{2}{*}{Scope} & CHANGED & 62.80\% & 38.46\% & 47.62\% & 90.60\% \\  & UNCHANGED & 92.66\% & 97.12\% & 94.81\% & \\ \hline \multirow{2}{*}{User Interaction} & REGURRED & 75.00\% & 44.44\% & 58.81\% & 83.76\% \\  & NONE & 85.15\% & 95.56\% & 90.00\% & \\ \hline \multirow{2}{*}{Privilege Required} & NONE & 90.00\% & 93.75\% & 91.84\% & 84.21\% \\  & LOW & 80.65\% & 75.76\% & 78.13\% & \\ \hline \multirow{2}{*}{Confidentiality} & NONE & 83.33\% & 88.71\% & 84.51\% & 75.58\% \\  & LOW & 68.97\% & 68.97\% & 68.97\% & \\ \hline \multirow{2}{*}{Integrity} & HIGH & 71.43\% & 68.18\% & 69.77\% & \\ \hline \multirow{2}{*}{Integrity} & NONE & 83.33\% & 83.33\% & 83.33\% & 73.17\% \\  & LOW & 66.67\% & 73.33\% & 69.84\% & \\ \hline \multirow{2}{*}{Availability} & NONE & 68.42\% & 59.09\% & 63.41\% & \\ \hline \multirow{2}{*}{Availability} & NONE & 72.00\% & 78.26\% & 75.00\% & 65.15\% \\  & LOW & 65.22\% & 60.00\% & 62.50\% & \\ \cline{1-1}  & HIGH & 52.63\% & 55.56\% & 54.08\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: DeepSeek evaluation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Class** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multirow{2}{*}{Attack Vector} & NETWORK & 97.14\% & 98.08\% & 97.60\% & 95.73\% \\  & LOCAL & 83.33\% & 76.92\% & 80.00\% & \\ \hline \multirow{2}{*}{Attack Complexity} & LOW & 94.83\% & 95.65\% & 95.24\% & 90.98\% \\  & HIGH & 16.67\% & 14.29\% & 15.38\% & \\ \hline \multirow{2}{*}{Scope} & CHANGED & 61.54\% & 40.00\% & 48.48\% & 86.07\% \\  & UNCHANGED & 89.00\% & 95.10\% & 91.95\% & \\ \hline \multirow{2}{*}{User Interaction} & REQUIRED & 82.35\% & 43.75\% & 57.14\% & 82.50\% \\  & NONE & 82.52\% & 96.59\% & 89.01\% & \\ \hline \multirow{2}{*}{Privilege Required} & NONE & 87.72\% & 90.91\% & 89.29\% & 80.37\% \\  & LOW & 77.78\% & 73.68\% & 75.68\% & \\  & HIGH & 57.14\% & 57.14\% & 57.14\% & \\ \hline \multirow{2}{*}{Confidentiality} & NONE & 83.33\% & 87.50\% & 85.37\% & 77.55\% \\  & LOW & 69.44\% & 75.76\% & 72.46\% & \\ \cline{1-1}  & HIGH & 80.00\% & 64.00\% & 71.11\% & \\ \hline \multirow{2}{*}{Integrity} & NONE & 80.00\% & 80.00\% & 80.00\% & 71.43\% \\ \cline{1-1}  & LOW & 69.77\% & 75.00\% & 72.29\% & \\ \cline{1-1}  & HIGH & 60.00\% & 52.17\% & 55.81\% & \\ \hline \multirow{2}{*}{Availability} & NONE & 68.97\% & 74.07\% & 71.43\% & 63.75\% \\ \cline{1-1}  & LOW & 66.67\% & 60.00\% & 63.16\% & \\ \cline{1-1}  & HIGH & 54.17\% & 56.52\% & 55.32\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Gemini evaluation. significantly with minority classes (e.g., ADJACENT Attack Vector for ChatGPT, HIGH complexity across all models, CHANGED Scope, and REQUIRED User Interaction). This pattern is attributed to the inherent class imbalance in the dataset, where models tended to prioritize the more frequent categories. * Difficulty with \"HIGH\" Class Predictions: A universal challenge for all three models was the accurate prediction of the \"HIGH\" class across various attributes, particularly Attack Complexity, Privileges Required, Confidentiality Impact, Integrity Impact, and Availability Impact. For instance, ChatGPT completely failed to predict HIGH complexity (0% precision, recall, and F1), and DeepSeek also showed 0% for this class. Gemini, while not zero, still had an abysmal F1 score of 15.38% for HIGH complexity.