Datasets:

nisaefendioglu
/

synthetic-sensitive-data-in-source-code-n300

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.