Model Card for PenTest-AI

Model Details

Model Description

PenTest-AI is a fine-tuned transformer model based on FacebookAI/xlm-roberta-large. It is specifically designed for text classification within the cybersecurity domain, with a primary focus on penetration testing phase detection. By analyzing security logs, audit reports, or threat intelligence texts, the model can categorize the information into distinct phases of the penetration testing lifecycle (e.g., Reconnaissance, Scanning, Exploitation, Post-Exploitation).

• Developed by: [Your Name/Organization]
• Funded by: [Your Organization/Grant Info, or "N/A"]
• Model type: Transformer-based Text Classification
• Language(s) (NLP): English (en)
• License: MIT
• Finetuned from model: FacebookAI/xlm-roberta-large

Model Sources

• Repository: [Link to your Hugging Face or GitHub Repo]
• Paper: [Link to paper, if applicable]
• Demo: [Link to demo space, if applicable]

Uses

Direct Use

The model is intended to be used by cybersecurity professionals, SOC analysts, and automated security pipelines to classify and tag security-related text data. It helps in automatically mapping unstructured text to specific penetration testing phases, streamlining reporting and threat analysis.

Downstream Use

PenTest-AI can be integrated into larger cybersecurity platforms, such as SIEM (Security Information and Event Management) systems or automated report generators, to provide context and phase-tagging for ingested alerts and logs.

Out-of-Scope Use

This model is intended for defensive and analytical purposes only. It is not designed to generate exploits, conduct automated attacks, or execute active offensive security measures. Furthermore, it should not be solely relied upon for critical incident response decisions without human oversight.

Bias, Risks, and Limitations

While PenTest-AI achieves high accuracy, it inherits the biases present in its training data and the base XLM-RoBERTa model. The model's performance may degrade when analyzing highly obfuscated text, non-standard terminology, or logs from proprietary tools not represented in the training set. False positives and false negatives are possible, so human verification is recommended for critical security assessments.

How to Get Started with the Model

Use the code below to get started with the model via the transformers library.

from transformers import pipeline

# Initialize the pipeline
classifier = pipeline("text-classification", model="[your-huggingface-username]/PenTest-AI")

# Example text
text = "The attacker utilized Nmap to discover open ports on the target subnet."

# Get predictions
result = classifier(text)
print(result)

Training DetailsTraining DataThe model was trained on a proprietary/open-source dataset consisting of [describe dataset, e.g., thousands of sanitized penetration testing reports, CVE descriptions, and security write-ups]. The dataset was curated to represent various phases of the penetration testing lifecycle.(See [Dataset Card Link] for more details).Training ProcedureTraining HyperparametersTraining regime: [e.g., fp16 mixed precision]Epochs: [Number of epochs]Batch Size: [Batch size]Learning Rate: [Learning rate, e.g., 2e-5]EvaluationTesting Data, Factors & MetricsThe model was evaluated on a held-out test set from the original training corpus, focusing on accurate phase detection across diverse writing styles and technical tooling mentions.Metrics Used:Accuracy: To measure overall correctness.F1 Score: To balance precision and recall, especially if classes were imbalanced.Precision & Recall: To understand the model's reliability in positively identifying specific phases without over-tagging.ResultsThe model achieved the following performance metrics on the evaluation set:MetricValueLoss0.3195Accuracy0.9318F1 Score0.8989Precision0.8683Recall0.9318Environmental ImpactCarbon emissions can be estimated using the Machine Learning Impact calculator.Hardware Type: [e.g., 1x NVIDIA A100]Hours used: [e.g., 12 hours]Cloud Provider: [e.g., AWS / GCP]Compute Region: [e.g., us-east-1]Carbon Emitted: [e.g., 0.5 kg eq. CO2]CitationIf you use this model in your research or project, please cite it as follows:BibTeX:Code snippet@misc{pentestai2024,
  author = {MattP8638/Organization]},
  title = {PenTest-AI: A Fine-Tuned XLM-RoBERTa Model for Penetration Testing Phase Detection},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/](https://huggingface.co/)[your-huggingface-username]/PenTest-AI}}
}