RISys-Lab/CyberSec-Text-Classification-ModernBert-Base

Model Card: Cybersecurity Text Classifier (ModernBERT-base)

"RedSage: A Cybersecurity Generalist LLM" (ICLR 2026)
Authors: Naufal Suryanto^1*, Muzammal Naseer¹, Pengfei Li¹, Syed Talal Wasim², Jinhui Yi², Juergen Gall², Paolo Ceravolo³, Ernesto Damiani^3
1Khalifa University, ²University of Bonn, ³University of Milan
^*Project Lead

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Model Details

• Model Type: Binary text classification model developed for domain-specific content filtering.
• Architecture: Based on ModernBERT-base, a bidirectional transformer encoder optimized for efficiency and long-context performance.
• Domain: Cybersecurity vs. Non-Cybersecurity.
• License: Released as part of the open-source RedSage project resources.

Intended Use

• Primary Use Case: Identifying cybersecurity-relevant documents within large-scale, unstructured web corpora such as FineWeb.
• Application: Filtering approximately 17.2 trillion tokens from Common Crawl subsets (2013–2024) to curate the 11.7B-token CyberFineWeb corpus.
• Intended Users: Researchers and developers focused on domain continual pretraining for cybersecurity LLMs.

Training Data

• Source Dataset: Cybersecurity Topic Classification dataset.
• Data Origin: Labeled samples collected from Reddit, StackExchange, and arXiv, alongside web articles.
• Dataset Size:
  • Pre-processing: 9.27M training samples and 459K validation samples.
  • Post-filtering: Reduced to 4.62M training samples and 2.46K validation samples after removing very short texts to minimize ambiguity.
• Labeling Method: Derived from forum categories, tags, and keyword metadata rather than LLM-generated annotations.

Training Procedure

• Optimizer: Adam optimizer.
• Learning Rate: 2e-5.
• Schedule: 10% warmup ratio over 2 training epochs.
• Hardware: Implementation utilized the ModernBERT-base encoder as the foundation for the binary head.

Evaluation Results

The model was evaluated on a validation set of 2,460 samples derived from web articles, achieving the following metrics:

Metric	Score
Accuracy	97.3%
Precision	92.8%
Recall	90.2%
F1 Score	91.4%

Limitations & Risks

• Context Sensitivity: While highly accurate, the model was specifically filtered to exclude very short texts to avoid context ambiguity.
• Temporal Bias: The model identifies cybersecurity content based on trends observed in web data up to late 2024; emerging threats post-2024 may not be represented.
• Dual-Use Concerns: The classifier is designed to identify offensive security technical content, which carries an inherent risk of misuse if applied outside of defensive or educational research.

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Citation

@inproceedings{suryanto2026redsage,
  title={RedSage: A Cybersecurity Generalist {LLM}},
  author={Naufal Suryanto and Muzammal Naseer and Pengfei Li and Syed Talal Wasim and Jinhui Yi and Juergen Gall and Paolo Ceravolo and Ernesto Damiani},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=W4FAenIrQ2}
}