| ---
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| license: gpl-2.0
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| language:
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| - en
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| tags:
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| - cybersecurity
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| - software-security
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| - vulnerability-detection
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| - java
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| - code-classification
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| - llm
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| - fine-tuning
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| task_categories:
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| - text-classification
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| - text-generation
|
| ---
|
|
|
| # OWASP Benchmark Java – LLM Fine-Tuning Dataset
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|
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| A chat-formatted dataset derived from the **OWASP Benchmark for Java** and prepared for large language model fine-tuning for Java vulnerability detection.
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|
|
| ## Dataset Summary
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| This dataset contains **21,041 Java security analysis examples** derived from OWASP Benchmark test cases.
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| Each example contains:
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| - A system instruction describing the security analysis task
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| - A Java source-code sample
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| - An assistant response containing:
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| - `vulnerable`: vulnerability presence (`true` / `false`)
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| - `cwe`: CWE identifier
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| - `category`: vulnerability category
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| The data is formatted using the `messages` structure commonly used for conversational and instruction fine-tuning.
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|
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| ## Dataset Structure
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|
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| | Split | Examples |
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| |---|---:|
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| | Train | 16,849 |
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| | Validation | 2,096 |
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| | Test | 2,096 |
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| | **Total** | **21,041** |
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|
|
| ## Example
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|
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| ```json
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| {
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| "messages": [
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| {
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| "role": "system",
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| "content": "You are a Java application security analyzer..."
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| },
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| {
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| "role": "user",
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| "content": "Analyze the following Java source code..."
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| },
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| {
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| "role": "assistant",
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| "content": "{\"vulnerable\": true, \"cwe\": \"CWE-501\", \"category\": \"trustbound\"}"
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| }
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| ]
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| }
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| ```
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|
|
| ## Labels
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| The dataset contains the following CWE categories:
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|
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| - CWE-22 – Path Traversal
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| - CWE-78 – OS Command Injection
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| - CWE-79 – Cross-Site Scripting
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| - CWE-89 – SQL Injection
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| - CWE-90 – LDAP Injection
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| - CWE-327 – Broken/Risky Cryptographic Algorithm
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| - CWE-328 – Weak Hashing
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| - CWE-330 – Weak Randomness
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| - CWE-501 – Trust Boundary Violation
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| - CWE-614 – Sensitive Cookie Without 'Secure' Flag
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| - CWE-643 – XPath Injection
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|
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| The primary binary classification target is:
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|
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| ```text
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| vulnerable = true / false
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| ```
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|
|
| ## Intended Use
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| The dataset is intended for:
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|
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| - Java source-code vulnerability detection
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| - Security-focused LLM fine-tuning
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| - Research on code security classification
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| - Evaluation of vulnerability-aware language models
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| - CWE classification experiments
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|
|
| ## Data Source
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|
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| The underlying source is the **OWASP Benchmark for Java**, an open-source benchmark designed to evaluate the accuracy, coverage, and speed of software vulnerability detection tools.
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| The OWASP Benchmark project states that its test cases contain intentionally vulnerable and non-vulnerable examples mapped to specific CWEs.
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| Source project:
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| https://github.com/OWASP-Benchmark/BenchmarkJava
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| Project information:
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| https://owasp.org/www-project-benchmark/
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|
|
| ## Dataset Preparation
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|
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| The original OWASP Benchmark Java examples were transformed into a conversational JSONL format suitable for LLM fine-tuning.
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| The transformation:
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| 1. Extracted the Java source code from each benchmark test.
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| 2. Associated each example with its corresponding CWE.
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| 3. Preserved the vulnerable/non-vulnerable label.
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| 4. Preserved the vulnerability category.
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| 5. Converted each example into a `system / user / assistant` message structure.
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| 6. Created train, validation and test splits.
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| No model-generated labels were used.
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|
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| ## Split
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|
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| The dataset contains:
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|
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| - `train.jsonl`
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| - `validation.jsonl`
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| - `test.jsonl`
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|
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| The current split contains approximately 80% training data, 10% validation data and 10% test data.
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|
|
| ### Distribution
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| The CWE distribution is approximately consistent across the three splits.
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| The vulnerable/non-vulnerable distribution is also approximately consistent across the splits.
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|
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| ## Important Note About Benchmark Similarity
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| OWASP Benchmark is a synthetic security benchmark containing many test cases based on common Java application structures and vulnerability patterns.
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| Consequently, structurally similar source-code examples may occur across different splits.
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| This dataset should therefore be interpreted as an OWASP Benchmark-based fine-tuning dataset rather than as a leakage-controlled real-world software corpus.
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| For rigorous generalization studies, evaluation on an independent real-world Java vulnerability dataset is recommended.
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|
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| ## Limitations
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| This dataset is based on a synthetic benchmark rather than naturally occurring production vulnerabilities.
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| Performance on this dataset should not be interpreted as equivalent to performance on real-world enterprise Java applications.
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| Models trained on this dataset may learn benchmark-specific code patterns.
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| For this reason, results should preferably be reported together with evaluation on additional independent datasets.
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|
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| ## License
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| The underlying OWASP Benchmark for Java is distributed under the **GNU General Public License v2.0 (GPL-2.0)**.
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| Users should review the original project's license and terms before redistributing or using derivative datasets.
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| The original project and its license information are available at:
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| https://github.com/OWASP-Benchmark/BenchmarkJava
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|
|
| ## Attribution
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|
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| This dataset is derived from the OWASP Benchmark Project.
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| OWASP Benchmark Project:
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| https://owasp.org/www-project-benchmark/
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| OWASP Benchmark for Java:
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| https://github.com/OWASP-Benchmark/BenchmarkJava
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|
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| This repository does not claim ownership of the original OWASP Benchmark source material.
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|
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| ## Citation
|
|
|
| If you use this dataset, please cite the original OWASP Benchmark project in addition to citing this dataset repository.
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|
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| ## Disclaimer
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| This dataset is intended for research and security engineering purposes.
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| The presence of a vulnerability label reflects the OWASP Benchmark's intended test classification and does not constitute a security assessment of production software. |
