--- dataset_info: features: - name: cve_id dtype: string - name: cve_published dtype: timestamp[ns] - name: cve_descriptions dtype: string - name: cve_metrics struct: - name: cvssMetricV2 list: - name: acInsufInfo dtype: bool - name: baseSeverity dtype: string - name: cvssData struct: - name: accessComplexity dtype: string - name: accessVector dtype: string - name: authentication dtype: string - name: availabilityImpact dtype: string - name: baseScore dtype: float64 - name: confidentialityImpact dtype: string - name: integrityImpact dtype: string - name: vectorString dtype: string - name: version dtype: string - name: exploitabilityScore dtype: float64 - name: impactScore dtype: float64 - name: obtainAllPrivilege dtype: bool - name: obtainOtherPrivilege dtype: bool - name: obtainUserPrivilege dtype: bool - name: source dtype: string - name: type dtype: string - name: userInteractionRequired dtype: bool - name: cvssMetricV30 dtype: 'null' - name: cvssMetricV31 list: - name: cvssData struct: - name: attackComplexity dtype: string - name: attackVector dtype: string - name: availabilityImpact dtype: string - name: baseScore dtype: float64 - name: baseSeverity dtype: string - name: confidentialityImpact dtype: string - name: integrityImpact dtype: string - name: privilegesRequired dtype: string - name: scope dtype: string - name: userInteraction dtype: string - name: vectorString dtype: string - name: version dtype: string - name: exploitabilityScore dtype: float64 - name: impactScore dtype: float64 - name: source dtype: string - name: type dtype: string - name: cve_references list: - name: source dtype: string - name: tags sequence: string - name: url dtype: string - name: cve_configurations list: - name: nodes list: - name: cpeMatch list: - name: criteria dtype: string - name: matchCriteriaId dtype: string - name: versionEndExcluding dtype: string - name: versionEndIncluding dtype: string - name: versionStartExcluding dtype: 'null' - name: versionStartIncluding dtype: string - name: vulnerable dtype: bool - name: negate dtype: bool - name: operator dtype: string - name: operator dtype: string - name: url dtype: string - name: cve_tags sequence: string - name: domain dtype: string - name: issue_owner_repo sequence: string - name: issue_body dtype: string - name: issue_title dtype: string - name: issue_comments_url dtype: string - name: issue_comments_count dtype: int64 - name: issue_created_at dtype: timestamp[ns] - name: issue_updated_at dtype: string - name: issue_html_url dtype: string - name: issue_github_id dtype: int64 - name: issue_number dtype: int64 - name: label dtype: bool - name: issue_msg dtype: string - name: issue_msg_n_tokens dtype: int64 - name: issue_embedding sequence: float64 splits: - name: post_train num_bytes: 62429952 num_examples: 2166 - name: post_test num_bytes: 41396204 num_examples: 1445 download_size: 71766293 dataset_size: 103826156 configs: - config_name: default data_files: - split: post_train path: data/post_train-* - split: post_test path: data/post_test-* ---

πŸ›‘οΈ GitHub Issues Vulnerability Detection

[![Paper](https://img.shields.io/badge/Paper-Arxiv%3A2501.05258-B31B1B?style=flat&logo=arxiv)](https://arxiv.org/abs/2501.05258) [![License](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) [![Python](https://img.shields.io/badge/Language-Python-blue.svg)](https://www.python.org/) **A benchmark dataset for automating the detection of code vulnerabilities by analyzing GitHub Issues.** *Curated to validate the findings in [Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues](https://arxiv.org/abs/2501.05258) (2025).*
--- ## πŸ“– Dataset Description **GitHub Issues Vulnerability Detection** is a specialized dataset designed to evaluate the feasibility of identifying software vulnerabilities early by analyzing textual discussions in GitHub issues. Traditional vulnerability detection relies heavily on static code analysis or community reporting after patches are applied. However, early indicators of vulnerabilities often appear in informal communication channels like GitHub issues before they are officially recognized. This dataset targets this "pre-disclosure" window by linking real-world GitHub discussions to confirmed CVEs. This dataset was created to support research into **Transformer-based vulnerability detection**, enabling models to distinguish between standard bug reports and critical security flaws based solely on textual descriptions. ### ⚑ Key Features * **Novelty:** The first dataset to map GitHub issues directly to CVE records for automated classification. * **Real-World Data:** Sourced from 31 top open-source repositories known for high vulnerability tracking activity. * **Balanced Context:** Includes both vulnerability-related issues (positive samples) and standard non-security bugs (negative samples) to reflect realistic noise ratios. * **Rich Metadata:** Contains full CVE metrics (CVSS scores), issue descriptions, and embeddings. * **Time-Aware Split:** Specifically split to avoid data leakage regarding model training cutoffs (post-September 2021). --- ## πŸ“‚ Dataset Structure ### Data Instances Each instance represents a **GitHub issue** paired with ground truth labels indicating whether it is linked to a confirmed CVE vulnerability. ### πŸ“Š Data Fields This dataset includes comprehensive metadata from both the National Vulnerability Database (NVD) and GitHub API. | Field | Type | Description | | :--- | :--- | :--- | | `issue_github_id` | `int64` | Unique GitHub identifier for the issue. | | `issue_title` | `string` | The title of the GitHub issue. | | `issue_body` | `string` | The full textual content/description of the issue. | | `issue_msg` | `string` | The processed message used for model input (title + body). | | `label` | `bool` | **True** if the issue is a vulnerability; **False** otherwise. | | `cve_id` | `string` | The CVE identifier (e.g., CVE-2023-XXXX) if applicable. | | `cve_descriptions` | `string` | Official description of the vulnerability from the NVD. | | `cve_published` | `timestamp` | Date the CVE was officially published. | | `cve_metrics` | `struct` | Detailed CVSS scoring metrics (V2, V3.1) assessing severity. | | `issue_created_at` | `timestamp` | Date the GitHub issue was created. | | `issue_owner_repo` | `sequence` | The `[owner, repo]` pair for the source repository. | | `issue_embedding` | `sequence` | Pre-computed embeddings for the issue text. | | `issue_msg_n_tokens`| `int64` | Token count of the issue message. | --- ## πŸ› οΈ Dataset Creation ### Curation Rationale Zero-day vulnerabilities and embargoed flaws are often discussed as "bugs" before they are officially labeled as vulnerabilities. Detecting these early can significantly reduce the window of exploitation. This dataset enables the training of LLMs and classifiers to flag these high-risk issues automatically. ### Source Data * **Primary Source:** GitHub Issues from open-source repositories. * **Ground Truth:** [National Vulnerability Database (NVD)](https://nvd.nist.gov/). * **Selection Criteria:** * **Repositories:** Ranked by the number of associated vulnerabilities; the top 31 were selected. * **Linkage:** Issues were linked to CVEs via external references in NVD entries. * **Length:** Issues exceeding 8,191 tokens were excluded to fit standard LLM context windows. ### Dataset Splits To ensure fair evaluation against models like GPT-3.5-Turbo (which has a training cutoff of Sept 2021), the dataset is split chronologically: * **Post-Cutoff Train:** Issues created after the cutoff, used for training classifiers and fine-tuning. * **Post-Cutoff Test:** Issues created after the cutoff, used exclusively for evaluation. * *Note: Issues pre-dating September 2021 were excluded to prevent data contamination.* --- ## πŸ“š Citation If you use this dataset, please cite the original paper: ```bibtex @article{cipollone2025automating, title={Automating the Detection of Code Vulnerabilities by Analyzing GitHub Issues}, author={Cipollone, Daniele and Izadi, Maliheh and Wang, Changjie and Scazzariello, Mariano and Ferlin, Simone and KostiΔ‡, Dejan and Chiesa, Marco}, journal={arXiv preprint arXiv:2501.05258}, year={2025} }