---
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.