deeppass2-bert

DeepPass2-XLM-RoBERTa Fine-tuned for Secret Detection

Model Description

DeepPass2 is a fine-tuned version of xlm-roberta-base specifically designed for detecting passwords and secrets in documents through token classification. Unlike traditional regex-based approaches, this model understands context to identify both structured tokens (API keys, JWTs) and free-form passwords.

Developed by: Neeraj Gupta (SpecterOps)
Model type: Token Classification (Sequence Labeling)
Base model: xlm-roberta-base
Language(s): English
License: [Same as base model]
Fine-tuned with: LoRA (Low-Rank Adaptation) through Unsloth Blog post: What's Your Secret?: Secret Scanning by DeepPass2

Model Architecture

Base Model

• Architecture: XLM-RoBERTa-base (Cross-lingual RoBERTa)
• Parameters: ~278M (base model)
• Max sequence length: 512 tokens
• Hidden size: 768
• Number of layers: 12
• Number of attention heads: 12

LoRA Configuration

LoraConfig(
    task_type=TaskType.TOKEN_CLS,
    r=64,                    # Rank
    lora_alpha=128,          # Scaling parameter
    lora_dropout=0.05,       # Dropout probability
    bias="none",
    target_modules=["query", "key", "value", "dense"]
)

Intended Use

This model is the BERT based model used in the DeepPass2 blog.

Primary Use Case

• Secret Detection: Identify passwords, API keys, tokens, and other sensitive credentials in documents
• Security Auditing: Scan documents for potential credential leaks
• Data Loss Prevention: Pre-screen documents before sharing or publishing

Input

• Text documents of any length (automatically chunked into 300-400 token segments) for DeepPass2 complete tool
• Text string of 512 tokens for particular instance of input to the Model

Output

• Token-level binary classification:
  • 0: Non-credential token
  • 1: Credential/password token

Training Data

Dataset Composition

• Total examples: 23,000 (20,800 training, 2,200 testing)
• Document types: Synthetic Emails, technical documents, logs, configuration files
• Password sources:
  • Real breached passwords from CrackStation's "real human" dump
  • Synthetic passwords generated by LLMs
  • Structured tokens (API keys, JWTs, etc.)

Data Generation Process

1. Base Documents: 2,000 long documents (2000+ tokens each) generated using LLMs
   • 50% containing passwords, 50% without
2. Chunking: Documents split into 300-400 token chunks with random boundaries
3. Password Injection: Real passwords inserted using skeleton sentences:

   "Your account has been created with username: {user} and password: {pass}"
   

4. Class Balance: <0.3% of tokens are passwords (maintaining real-world distribution)

Training Procedure

Hardware

• Trained on MacBook Pro (64GB RAM) with MPS acceleration
• Can be trained on systems with 8-16GB RAM

Hyperparameters

• Epochs: 4
• Batch size: 8 (per device)
• Weight decay: 0.01
• Optimizer: AdamW (default in Trainer)
• Learning rate: Default (5e-5)
• Max sequence length: 512 tokens
• Random seed: 2

Training Process

# Preprocessing
- Tokenization with offset mapping
- Label generation based on credential spans
- Padding to max_length with truncation

# Fine-tuning
- LoRA adapters applied to attention layers
- Binary cross-entropy loss
- Token-level classification head

Performance Metrics

Chunk-Level Metrics

Password-Level Metrics

Definitions

• Strict Accuracy: All passwords in chunk detected with 100% accuracy
• Overlap Accuracy: At least one password detected with >30% overlap with ground truth

Limitations and Biases

Known Limitations

1. Context window: Limited to 512 tokens per chunk
2. Training data: Primarily trained on LLM-generated documents which may not fully represent real-world documents
3. Password types: Better at detecting structured/complex passwords than simple dictionary words
4. Tokenization boundaries: SentencePiece tokenization can fragment passwords, affecting boundary detection

Potential Biases

• May over-detect in technical documentation due to training distribution
• Tends to flag alphanumeric strings more readily than common words used as passwords

Ethical Considerations

Responsible Use

• Privacy: This model should only be used on documents you have permission to scan
• Security: Detected credentials should be handled securely and not logged or stored insecurely
• False Positives: Always verify detected credentials before taking action

Misuse Potential

• Should not be used to scan documents without authorization
• Not intended for credential harvesting or malicious purposes

Usage

Installation

pip install transformers torch

Quick Start

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "path/to/deeppass2-xlm-roberta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Classify tokens
def detect_passwords(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    predictions = torch.argmax(outputs.logits, dim=-1)
    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
    
    # Extract password tokens
    password_tokens = [
        token for token, label in zip(tokens, predictions[0])
        if label == 1
    ]
    
    return password_tokens

Integration with DeepPass2

For production use, integrate with the full DeepPass2 pipeline:

1. NoseyParker regex filtering
2. BERT token classification (this model)
3. LLM validation for false positive reduction

See the DeepPass2 repository for complete implementation.

Citation

@software{gupta2025deeppass2,
  author = {Gupta, Neeraj},
  title = {DeepPass2: Fine-tuned XLM-RoBERTa for Secret Detection},
  year = {2025},
  organization = {SpecterOps},
  url = {https://huggingface.co/deeppass2-bert},
  note = {Blog: \url{https://specterops.io/blog/2025/07/31/whats-your-secret-secret-scanning-by-deeppass2/}}
}

Additional Information

Model Versions

• v6.0-BERT: Current production version with LoRA adapters
• merged-model: LoRA weights merged with base model for easier deployment

Related Links

• DeepPass2 Blog Post
• Original DeepPass (2022)
• NoseyParker

Contact

For questions or issues, please open an issue on the GitHub repository