---
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.