DeepPass2

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Multilayered secret detection tool

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# DeepPass2

A multi-layer secrets detection system using regex patterns, fine-tuned BERT, and LLM verification.

- **Blog Post**: [What's Your Secret?: Secret Scanning by DeepPass2](https://specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/)
- **Model**: [Deeppass2-xlm-roberta](https://huggingface.co/gneeraj/deeppass2-bert)

## Overview

DeepPass2 combines regex rules, a fine-tuned BERT model, and LLM validation to detect both structured tokens and context-dependent free-form passwords in documents. It improves accuracy and reduces false positives by leveraging contextual understanding and a multi-tiered architecture.

![DeepPass2 Architecture](https://specterops.io/wp-content/uploads/sites/3/2025/07/image_533330.png)

*Multi-tier architecture: NoseyParker → BERT → LLM validation*

## Setup

### Requirements
```bash
pip install -r requirements.txt
```

### Required Files
- `deeppass2.py` - Main application
- `utils/BERTprocessor.py` - BERT token classification
- `utils/nprules.py` - Async regex checking
- `regexRules.jsonl` - Regex patterns from [Nosey Parker](https://github.com/praetorian-inc/noseyparker) (one pattern per line)
- Fine-tuned model at `path/to/merged-model` - Request model access from the Huggingface - [Huggingface](https://huggingface.co/gneeraj/deeppass2-bert)

### Environment Variables
Create `.env` file:
```
LITELLM_API_KEY=<YOUR LITE LLM API KEY>
LITELLM_BASE_URL=<YOUR CUSTOM LITELLM BASE URL LINK>
AUGMENT_MODEL=<MODEL NAME>
hf_token=<YOUR HF TOKEN>
DEEPPASS2=<HOST LINK>
```

## Running

```bash
python deeppass2.py
```

Server starts on `http://localhost:5000`

### API Usage
```bash
curl -X POST http://localhost:5000/api/deeppass2 \
  -H "Content-Type: text/plain" \
  --data-binary "@document.txt"
```

## How It Works

### Sequence Labeling Approach
![Sequence Labeling Example](https://specterops.io/wp-content/uploads/sites/3/2025/07/image_dd0509.png)

*BERT-based token classification identifies passwords using contextual understanding*

### Pipeline Flow
1. **Nosey Parker**: Regex pattern matching (based on [Nosey Parker](https://github.com/praetorian-inc/noseyparker) rules)
2. **Document Cleaning**: Remove regex matches to reduce false positives
3. **Chunking**: Split document into BERT-compatible chunks (300-400 tokens)
4. **BERT Classification**: Identify potential credentials using fine-tuned xlm-RoBERTa-base
5. **LLM Verification**: Confirm if detected tokens are actual secrets

### Performance Metrics
- **Strict Accuracy**: 86.67% (BERT) / 85.79% (LLM)
- **Overlap Accuracy**: 97.72% (BERT) / 95.35% (LLM)

## Customization

### 1. Change Model Path
Edit line 35 in `deeppass2.py`:
```python
model_name = "your-model-path"  # Local path or HuggingFace model ID
```

### 2. Use Different LLM Provider
Replace lines 60-64 with your LLM client:
```python
# Example: Direct OpenAI
import openai
openai.api_key = "your-key"

# Then modify get_secrets_LLM() function to use openai.ChatCompletion.create()
```

### 3. Adjust Chunking Parameters
Edit `chunk_document()` call parameters:
```python
chunks = chunk_document(doc_np_cleaned, tokenizer, 
                       max_len=512,      # Maximum tokens per chunk
                       min_len=300,      # Minimum tokens per chunk
                       overlap_ratio=0.1) # Overlap between chunks
```

Keep in mind that the BERT model is trained on these min and max lengths. Changing these could hamper the performance of the tool.

### 4. Change Device Priority
Modify lines 40-48 to force specific device:
```python
device = "cuda"  # Force CUDA
# device = "mps"   # Force Apple Silicon
# device = "cpu"   # Force CPU
```

### 5. Custom Regex Rules
Add patterns to `regexRules.jsonl`:
```json
{"name": "AWS Key", "id": "aws_1", "pattern": "AKIA[0-9A-Z]{16}"}
{"name": "GitHub Token", "id": "gh_1", "pattern": "ghp_[a-zA-Z0-9]{36}"}
```

### 6. Modify LLM Prompt
Edit `get_prompt()` function:
```python
def get_prompt(text, passwords):
    prompt = f"""Your custom prompt here
    Credentials: {passwords}
    Context: {text}
    """
    return prompt
```

Keep in mind that this might affect the performance of the tool.

### 7. Change Port
Last line of `deeppass2.py`:
```python
app.run(port=8080, debug=False)  # Change port and disable debug
```

## Response Format

### Example Output
![DeepPass2 Output](https://specterops.io/wp-content/uploads/sites/3/2025/07/image_2a40ab.png)

*DeepPass2 returns detected passwords with surrounding context for human review*

### JSON Structure
```json
{
  "Success": [
    {"Nosey Parker": [...]},
    {"BERT_secrets": [...]},
    {"LLM_scanning": [...]}
  ]
}
```

## References
- **Nosey Parker**: Secret detection regex patterns adapted from [Praetorian's Nosey Parker](https://github.com/praetorian-inc/noseyparker)
- **DeepPass (2022)**: Original character-level BiLSTM approach by Will Schroeder - [Finding Passwords with Deep Learning](https://posts.specterops.io/deeppass-finding-passwords-with-deep-learning-4d31c534cd00)

## Citation

If you use DeepPass2 in your research or work, please cite:

### BibTeX
```bibtex
@software{gupta2025deeppass2,
  author = {Gupta, Neeraj},
  title = {DeepPass2: Multi-layer Secrets Detection System},
  year = {2025},
  month = {7},
  organization = {SpecterOps},
  url = {https://github.com/SpecterOps/DeepPass2},
  note = {Blog post: \url{https://specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/}}
}
```

### APA
```
Gupta, N. (2025). DeepPass2: Multi-layer secrets detection system [Computer software]. SpecterOps. 
https://specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/
```

### MLA
```
Gupta, Neeraj. "DeepPass2: Multi-layer Secrets Detection System." SpecterOps, 31 July 2025, 
specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/.
```

### IEEE
```
N. Gupta, "DeepPass2: Multi-layer Secrets Detection System," SpecterOps, Jul. 2025. 
[Online]. Available: https://specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/
```

DeepPass2

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