| --- |
| license: mit |
| datasets: |
| - mahdin70/cwe_enriched_balanced_bigvul_primevul |
| metrics: |
| - accuracy |
| - precision |
| - f1 |
| - recall |
| base_model: |
| - microsoft/graphcodebert-base |
| library_name: transformers |
| --- |
| |
| # GraphCodeBERT-VulnCWE - Fine-Tuned GraphCodeBERT for Vulnerability and CWE Classification |
|
|
| ## Model Overview |
| This model is a fine-tuned version of **microsoft/graphcodebert-base** on a curated and enriched dataset for vulnerability detection and CWE classification. It is capable of predicting whether a given code snippet is vulnerable and, if vulnerable, identifying the specific CWE ID associated with it. |
|
|
| ## Dataset |
| The model was fine-tuned using the dataset [mahdin70/cwe_enriched_balanced_bigvul_primevul](https://huggingface.co/datasets/mahdin70/cwe_enriched_balanced_bigvul_primevul). The dataset contains both vulnerable and non-vulnerable code samples and is enriched with CWE metadata. |
|
|
| ### CWE IDs Covered: |
| 1. **CWE-119**: Improper Restriction of Operations within the Bounds of a Memory Buffer |
| 2. **CWE-20**: Improper Input Validation |
| 3. **CWE-125**: Out-of-bounds Read |
| 4. **CWE-399**: Resource Management Errors |
| 5. **CWE-200**: Information Exposure |
| 6. **CWE-787**: Out-of-bounds Write |
| 7. **CWE-264**: Permissions, Privileges, and Access Controls |
| 8. **CWE-416**: Use After Free |
| 9. **CWE-476**: NULL Pointer Dereference |
| 10. **CWE-190**: Integer Overflow or Wraparound |
| 11. **CWE-189**: Numeric Errors |
| 12. **CWE-362**: Concurrent Execution using Shared Resource with Improper Synchronization |
|
|
| --- |
|
|
| ## Model Training |
| The model was trained for **3 epochs** with the following configuration: |
| - **Learning Rate**: 2e-5 |
| - **Weight Decay**: 0.01 |
| - **Batch Size**: 8 |
| - **Optimizer**: AdamW |
| - **Scheduler**: Linear |
|
|
| ### Training Loss and Validation Metrics Per Epoch: |
| | Epoch | Training Loss | Validation Loss | Vul Accuracy | Vul Precision | Vul Recall | Vul F1 | CWE Accuracy | |
| |-------|---------------|-----------------|--------------|---------------|------------|--------|--------------| |
| | 1 | 1.2824 | 1.4160 | 0.7914 | 0.8990 | 0.5200 | 0.6589 | 0.3551 | |
| | 2 | 1.1292 | 1.2632 | 0.8007 | 0.8037 | 0.6426 | 0.7142 | 0.4433 | |
| | 3 | 0.8598 | 1.2436 | 0.7945 | 0.7669 | 0.6747 | 0.7179 | 0.4605 | |
|
|
| #### Training Summary: |
| - **Total Training Steps**: 5916 |
| - **Training Loss**: 1.2380 |
| - **Training Time**: 4785.0 seconds (~80 minutes) |
| - **Training Speed**: 9.89 samples per second |
| - **Steps Per Second**: 1.236 |
|
|
|
|
| ## How to Use the Model |
| ```python |
| from transformers import AutoModel, AutoTokenizer |
| |
| model = AutoModel.from_pretrained("mahdin70/GraphCodeBERT-VulnCWE", trust_remote_code=True) |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/graphcodebert-base") |
| |
| code_snippet = "int main() { int arr[10]; arr[11] = 5; return 0; }" |
| inputs = tokenizer(code_snippet, return_tensors="pt") |
| outputs = model(**inputs) |
| |
| vul_logits = outputs["vul_logits"] |
| cwe_logits = outputs["cwe_logits"] |
| |
| vul_pred = vul_logits.argmax(dim=1).item() |
| cwe_pred = cwe_logits.argmax(dim=1).item() |
| |
| print(f"Vulnerability: {'Vulnerable' if vul_pred == 1 else 'Non-vulnerable'}") |
| print(f"CWE ID: {cwe_pred if vul_pred == 1 else 'N/A'}") |
| ``` |
