| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| - de |
| - fr |
| - es |
| - ru |
| task_categories: |
| - text-classification |
| - token-classification |
| tags: |
| - security |
| - secret-detection |
| - credentials |
| - dlp |
| - code |
| - pii |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: corpus |
| data_files: corpus.parquet |
| default: true |
| - config_name: prowlbench |
| data_files: prowlbench.parquet |
| --- |
| |
| <p align="center"><img src="https://huggingface.co/datasets/Podric/prowl-secrets-corpus/resolve/main/logo.png" width="360" alt="Prowl"></p> |
|
|
| # Prowl secrets corpus |
|
|
| A labeled corpus for training and evaluating secret detectors (credentials, API keys, tokens, |
| database URIs, private keys, and passwords) across **code, Jira tickets, Confluence pages, chat, |
| and logs**, in multiple languages. It is the training data behind |
| [Prowl](https://github.com/Lercas/prowl) and its stage-3 |
| [encoder](https://huggingface.co/Podric/prowl-secret-encoder). |
|
|
| **503,027 records: 181,530 positive, 321,497 negative.** |
|
|
| > No live credentials. Every value is synthetic, format-preserving-obfuscated, or drawn from a public |
| > test fixture. The corpus is built so it can be handled without leaking a real secret. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("Podric/prowl-secrets-corpus") # full corpus (default config) |
| bench = load_dataset("Podric/prowl-secrets-corpus", "prowlbench") # the held-out evaluation config |
| |
| ex = ds["train"][0] # {"text", "value", "label_binary", "label_type", "span", "source", "origin", "context"} |
| ``` |
|
|
| ## Fields |
|
|
| | field | type | meaning | |
| |---|---|---| |
| | `text` | string | the fragment to classify (code line, config value, ticket/log/chat text) | |
| | `value` | string \| null | the secret substring, when positive | |
| | `label_binary` | 0 \| 1 | contains a secret | |
| | `label_type` | string \| null | secret type (`aws_access_key_id`, `stripe_secret_key`, `generic_password`, ...) | |
| | `span` | [int,int] \| null | `[start,end)` of `value` inside `text` (for NER) | |
| | `source` | string | channel: `code` \| `jira` \| `confluence` \| `log` \| `slack` | |
| | `origin` | string | provenance tag (see below) | |
| | `context` | string \| null | surrounding context, when available | |
|
|
| ## Composition by channel |
|
|
| | code | Jira | Confluence | log | chat | |
| |:--:|:--:|:--:|:--:|:--:| |
| | 302,153 | 97,122 | 80,092 | 19,883 | 3,777 | |
|
|
| Non-code (tickets, wiki, logs, chat) is ~40% of the corpus, the realistic-leak surface that |
| code-only datasets omit. |
|
|
| ## Provenance: where each part comes from |
|
|
| | origin | share | source | how it was made | |
| |---|:--:|---|---| |
| | `synthetic` | 73.8% | original | format- and checksum-correct fake secrets generated per type, injected into templated carriers at known spans | |
| | `creddata` | 8.4% | [Samsung/CredData](https://github.com/Samsung/CredData) | real-world credential samples, **character-class obfuscated** so format survives but no real value does | |
| | `hf` | 6.8% | [ai4privacy/pii-masking-200k](https://huggingface.co/datasets/ai4privacy/pii-masking-200k), [nvidia/Nemotron-PII](https://huggingface.co/datasets/nvidia/Nemotron-PII) | synthetic PII prose; passwords → positives, other PII → hard negatives (`neg_pii`) | |
| | `fleet*` / `authored` / `carrier` | 10.9% | original | synthetic carriers: code in 17 languages, 44 config formats, ticket/wiki/chat/log documents, hard negatives, obfuscation | |
| | `fixture` | <0.1% | gitleaks / detect-secrets / whispers test suites (public) | imported public test fixtures | |
|
|
| The `synthetic`, `fleet*`, `authored`, and `carrier` portions are original to this project. The |
| `creddata`, `hf`, and `fixture` portions are derived from the linked upstream datasets and remain |
| under their respective licenses (CredData; ai4privacy and Nemotron-PII per their HF cards; the tool |
| fixtures per each project's license). Attribute the originals if you redistribute. |
|
|
| ## Splits and leakage control |
|
|
| The corpus is meant to be split **value-disjoint** (no secret value appears in both train and eval) |
| and **origin-disjoint**: the real-secret sources (`creddata`, `hf`) are held out so a validation |
| score measures generalization, not memorization. The encoder's operating threshold is calibrated on |
| such a held-out split. |
|
|
| ## `prowlbench` config |
|
|
| The second config is [ProwlBench](https://github.com/Lercas/prowlbench): |
| 3,843 leakage-safe evaluation cases (structured tokens, generic high-entropy keys, multilingual |
| free-form prose) with per-case tier and language, used to compare scanners as real subprocesses. |
| It is value-disjoint from `corpus`. |
|
|
| ## Intended use |
|
|
| Training and evaluating secret/credential detectors and DLP classifiers. Not a source of real |
| credentials and not usable as one. |
|
|
| ## License |
|
|
| Noncommercial use only (CC BY-NC 4.0). Not for use in commercial products. The original portions |
| (`synthetic`, `fleet*`, `authored`, `carrier`) are released under CC BY-NC 4.0. The derived portions |
| (`creddata`, `hf`, `fixture`) stay under their upstream licenses, listed in the provenance table. |
| Attribute the originals if you redistribute. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{prowl_secrets_corpus, |
| title = {Prowl secrets corpus}, |
| author = {Prowl}, |
| year = {2026}, |
| howpublished = {Hugging Face Datasets}, |
| url = {https://huggingface.co/datasets/Podric/prowl-secrets-corpus} |
| } |
| ``` |
|
|
