language:
- en
tags:
- AI
- NLP
- Cybersecurity
- Ethical Hacking
- Pentesting
license: mit
pipeline_tag: text-generation
metrics:
- accuracy
- perplexity
- response_time
model_type: causal-lm
Model Card for Pentest AI
This model card provides an overview of Pentest AI, a generative language model designed to assist in the domain of penetration testing and cybersecurity. It generates informative responses related to ethical hacking practices and techniques, helping users enhance their knowledge and skills in the field.
Model Details
Model Description
Pentest AI is a causal language model fine-tuned specifically for generating relevant and contextual information about penetration testing methodologies, tools, and best practices. It serves as an educational resource for security professionals and enthusiasts.
- Developed by: Esteban Cara de Sexo
- Funded by [optional]: No funding received
- Shared by [optional]: [More Information Needed]
- Model type: Causal Language Model (CLM)
- Language(s) (NLP): English
- License: MIT
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [Your GitHub Repository Link]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
Pentest AI is intended for direct interaction, allowing users to generate and explore text-based scenarios related to penetration testing and cybersecurity techniques.
Downstream Use [optional]
This model can be incorporated into cybersecurity training platforms, interactive learning environments, or tools aimed at improving security practices.
Out-of-Scope Use
The model is not intended for use in malicious activities, unauthorized access, or any illegal operations related to penetration testing.
Bias, Risks, and Limitations
While Pentest AI aims to produce accurate information, it may generate biased or misleading content. Users are encouraged to critically evaluate the outputs.
Recommendations
Users should be aware of the model's limitations and verify generated content before application in real-world scenarios, especially concerning ethical and legal implications.
How to Get Started with the Model
To start using Pentest AI, you can implement the following code snippet:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "Canstralian/pentest_ai"
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
input_text = "Describe the steps involved in a penetration test."
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(inputs)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(output_text)
Training Details
Training Data
The model was trained on a diverse dataset encompassing articles, guides, and documentation related to penetration testing and cybersecurity. Refer to the associated Dataset Card for more details.
Training Procedure
Preprocessing [optional]
Training data was filtered to remove any sensitive or personally identifiable information, ensuring adherence to ethical standards.
Training Hyperparameters
- Training regime: fp16 mixed precision
Speeds, Sizes, Times [optional]
- Training Duration: Approximately 10 hours
- Checkpoint Size: 500MB
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated on a distinct dataset of penetration testing scenarios and inquiries.
Factors
Evaluation metrics are disaggregated by user demographics and application contexts, including educational versus professional uses.
Metrics
- Accuracy: Measures the correctness of the model's generated responses.
- Perplexity: Assesses the model's confidence in its predictions.
- Response Time: Measures how quickly the model provides outputs.
Results
The model demonstrated an accuracy of 85% in generating appropriate responses during evaluation.
Summary
Pentest AI proves to be a valuable resource for generating information on penetration testing, but users should remain cautious and validate the generated information.
Model Examination [optional]
Further research is required to assess the interpretability and decision-making processes of the model.
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: NVIDIA Tesla V100
- Hours used: 10
- Cloud Provider: Google Cloud Platform
- Compute Region: us-central1
- Carbon Emitted: Estimated 120 kg CO2
Technical Specifications [optional]
Model Architecture and Objective
Pentest AI employs a transformer architecture optimized for generating coherent and contextually relevant text in the realm of penetration testing.
Compute Infrastructure
The model was trained on high-performance GPU instances within a cloud infrastructure.
Hardware
- Type: NVIDIA Tesla V100
- Count: 4 GPUs
Software
The model is developed using PyTorch and the Hugging Face Transformers library.
Citation [optional]
For citations related to this model, please refer to the following information:
BibTeX:
@article{deJager2024,
title={Pentest AI: A Generative Model for Penetration Testing Text Generation},
author={Esteban Cara de Sexo},
journal={arXiv preprint arXiv:2401.00000},
year={2024}
}
APA:
Cara de Sexo, E. (2024). Pentest AI: A Generative Model for Penetration Testing Text Generation. arXiv preprint arXiv:2401.00000.
Glossary [optional]
- Causal Language Model (CLM): A model that predicts the next word in a sequence based on the previous words.
More Information [optional]
For further inquiries and updates, please refer to [Your GitHub Repository Link].
Model Card Authors [optional]
- Esteban Cara de Sexo
Model Card Contact
For questions, please contact Esteban Cara de Sexo at [distortedprojection@gmail.com].
### Next Steps
1. **Replace placeholders** with your actual information and links.
2. **Update metrics** and results based on your model's specific performance and findings.
3. **Review and edit sections** to ensure they accurately represent your model and its capabilities.