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license: mit
task_categories:
- text-classification
- other
language:
- en
- code
tags:
- security
- secrets
- pii
- llm
- owasp
- synthetic
- source-code
- masking
size_categories:
- n<1K
pretty_name: Synthetic Sensitive Data in Source Code (N=300)
---
# Synthetic Sensitive Data in Source Code (N=300)
Synthetic dataset of **300** source-code / config snippets containing **hardcoded secrets and PII**.
Every sample includes at least one sensitive finding (no clean negatives).
Designed for evaluating local masking, secret detection, and **OWASP LLM02 — Sensitive Information Disclosure** scenarios in AI-assisted coding workflows.
**Version 1.2:** `multi_secret` (and related) samples label **every** secret present in `code_text` (complete ground truth).
> All values are **synthetic / fake**. Do not treat them as real credentials.
## Files
| File | Description |
|------|-------------|
| `synthetic_sensitive_data_in_source_code_n300.json` | Full records + ground-truth `sensitive_findings` |
| `synthetic_sensitive_data_in_source_code_n300.csv` | Flat view (`\n` escaped) |
| `synthetic_sensitive_data_in_source_code_n300_excel.csv` | Excel-friendly (comma + BOM) |
| `synthetic_sensitive_data_in_source_code_n300_tr.csv` | Turkish Excel (semicolon + BOM) |
| `synthetic_sensitive_data_in_source_code_n300.xlsx` | Excel workbook |
| `dataset_stats.json` | Distribution summary |
## Categories (N=300)
| Category | Count |
|----------|------:|
| `api_key` | 55 |
| `password_secret` | 50 |
| `connection_string` | 45 |
| `pii` | 45 |
| `internal_url` | 40 |
| `multi_secret` | 35 |
| `private_key_keystore` | 30 |
Languages include Python, JavaScript, Java, C#, Kotlin, Go, Bash, env, YAML, and JSON.
## Schema
- `id` — sample id (`SDS-####`)
- `category` — primary category
- `language` — snippet language
- `code_text` — raw code/config (model input)
- `sensitive_count` — number of labeled secrets
- `finding_types` — secret types joined by `|` (CSV)
- `sensitive_findings` — ground-truth list (JSON only)
OWASP alignment is dataset-level (`LLM02`); there is no per-row OWASP column.
## Intended use
- Secret / PII detection benchmarks
- Local masking and reverse-masking evaluation
- Prompt/code leakage experiments with LLMs
## Notes
- Fully synthetic; reproducible with `seed=42`
- Average secrets per sample ≈ 1.29
- Not a production vulnerability corpus
## Citation
If you use this dataset, please cite the related thesis / paper work by Nisa Nur Efendioğlu.
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