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chenyiren/
DeepSeek-7B-base_honeywords

Name: DeepSeek-7B-base_honeywords
Availability: InStock

safetensors

llama

apache-2.0

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DeepSeek-7B-base_honeywords

A fine-tuned model for password strength analysis and honeyword generation

Model Description

DeepSeek-7B-base_honeywords is a fine-tuned version of the DeepSeek-7B-llm-base model, specifically designed for password security analysis and honeyword generation. The model has been trained on over 3,000 password-related Q&A pairs to enhance its ability to:

Evaluate Password Strength: Given a plaintext password, the model determines whether it is strong or weak, providing a detailed explanation based on factors such as length, character variety, and predictability.
Generate Honeywords: The model generates a set of decoy passwords (honeywords) that can be stored alongside the real password. These honeywords act as security traps—if an intruder attempts to use them, security administrators can detect unauthorized access attempts and trace the attacker's IP.

Model Applications

This model is particularly useful for:
✅ Enterprise Security & Compliance: Identify weak passwords and enforce stronger password policies.
✅ Deception-Based Security: Generate honeywords to enhance authentication security and detect credential leaks.
✅ Penetration Testing & Cybersecurity Research: Evaluate password security measures and improve defensive strategies.

How to Use

You can load and interact with the model via Hugging Face’s Transformers library:

1. Load the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "chenyiren/DeepSeek-7B-base_honeywords"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

2. Password Strength Analysis

python

def evaluate_password(password):
    prompt = f"Is the password '{password}' strong or weak? Explain why."
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(**inputs, max_length=150)
    print(tokenizer.decode(output[0], skip_special_tokens=True))

evaluate_password("qwe123")

3. Generate Honeywords

python

def generate_honeywords(password, num_honeywords=5):
    prompt = f"Generate {num_honeywords} honeywords for the password '{password}'."
    inputs = tokenizer(prompt, return_tensors="pt")
    output = model.generate(**inputs, max_length=100)
    print(tokenizer.decode(output[0], skip_special_tokens=True))

generate_honeywords("P@ssw0rd!")

Training Details

Base Model: DeepSeek-7B-llm-base
Fine-Tuning Dataset: 3,000+ password-related Q&A pairs
Optimization Method: LoRA fine-tuning
Inference Engine: Supports huggingface/transformers https://github.com/JJJiangYH/LLaMA-Factory-

Limitations & Considerations

The model's password strength evaluation follows general security best practices but should not replace dedicated security audits.
Honeyword effectiveness depends on integration with authentication systems.
The model does not store or remember user passwords.
The model effect may be affected by prompt words and become unstable.If the model does not perform well in the current hardware environment, you can use the question answer pairs stored in the repository to train the local model, or obtain more high-quality question answer pairs through model distillation.

License

This model is released under [insert license type, e.g., Apache 2.0, MIT, or custom license].