---
license: apache-2.0
library_name: onnx
pipeline_tag: token-classification
tags:
- secret-detection
- password-detection
- credentials
- api-keys
- security
- onnx
language:
- en
- pl
- de
- es
- fr
---

# password-detector

**The main idea: keep it small and fast.** A model that runs anywhere on a CPU, with no GPU and no heavy dependencies.

A tiny model that finds passwords, API keys, tokens and private keys in any text: code, configs, logs, emails,
chats and documents. Byte-level CNN, no tokenizer, runs on one CPU core with `numpy` + `onnxruntime`.
From **97 KB** (tiny) to **0.7 MB** (recommended cascade).

## Quick start

```bash
pip install huggingface_hub numpy "onnxruntime>=1.17"
huggingface-cli download bardsai/password-detector --local-dir password-detector && cd password-detector
```

```python
from password_detector.detector import SecretDetector

det = SecretDetector("onnx/default/model_int8.onnx,onnx/large/model_int8.onnx",
                     prefilter="onnx/tiny/model_int8.onnx")
det.scan("Cześć, hasło do VPN to kawa2024!biuro")
# [{'value': 'kawa2024!biuro', 'score': 1.0, 'type': 'password', 'start': 23, 'end': 37}]
det.scan("password = os.environ['DB_PASSWORD']")
# []
```

CLI (values masked unless `--show`; exit code 1 when anything is found):

```bash
python -m password_detector --model onnx/tiny/model_int8.onnx path/to/files
```

Types: `password`, `api_key`, `token`, `private_key`, `generic`. Default threshold 0.5.

## Results

Offline, one CPU core, int8 models at threshold 0.5.

| Model | Size | CredData F1 | GitHub issues F1 | Docs F1 | Speed |
|---|---|---|---|---|---|
| **cascade** (tiny → default + large) | 0.7 MB | **0.89** | 0.85 | **0.94** | 138 KB/s |
| tiny (`onnx/tiny`) | 97 KB | 0.87 | 0.85 | **0.94** | 137 KB/s |
| default (`onnx/default`) | 219 KB | 0.88 | **0.87** | 0.93 | 66 KB/s |
| CredSweeper 1.19 * | 12 MB | 0.94 | 0.79 | 0.41 | 230 KB/s |
| detect-secrets 1.5 | 1.1 MB | 0.57 | 0.48 | 0.32 | 155 KB/s |
| gitleaks 8 | 23 MB | 0.44 | 0.69 | 0.25 | 958 KB/s |

CredData: 64 held-out repos of [Samsung CredData](https://github.com/Samsung/CredData). GitHub issues:
[Ahmed et al. 2025](https://arxiv.org/abs/2410.23657). Docs: synthetic emails, chats and tickets in 5 languages.
\* CredSweeper was trained on all of CredData, so its CredData score is not held out.

## Limitations

- Passwords in prose with unusual wording (short PINs, "access code X") can score below 0.5.
- Hex strings and UUIDs next to words like `secret` sometimes raise false alarms.
- Does not verify tokens with providers or detect secrets split across lines.

Training data: CredData training repos, synthetic code, configs and documents (randomly generated secrets) and
real non-secret text. No benchmark data was used for training.