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README.md

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