Cmdcaliper Large

🚀 CmdCaliper-large

The CmdCaliper models, developed by CyCraft AI Lab, are the first embedding models specifically tailored for command - line embeddings. Even the smallest version of CmdCaliper, with around 30 million parameters, can outperform state - of - the - art sentence embedding models with over 10 times more parameters (335 million) in various command - line - specific tasks. CmdCaliper offers three models of different sizes (CmdCaliper - large, CmdCaliper - base, and CmdCaliper - small), providing flexible options for different hardware resource constraints. It was introduced in the EMNLP 2024 paper titled "CmdCaliper: A Semantic - Aware Command - Line Embedding Model and Dataset for Security Research".

📚 Documentation

[Dataset] [[Code](https://github.com/cycraft - corp/CmdCaliper)] [Paper]

✨ Features

The CmdCaliper models are specifically designed for command - line embeddings. They offer different sizes to suit various hardware resource constraints and can achieve excellent performance in command - line - specific tasks even with relatively fewer parameters.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

HuggingFace Transformers

import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

input_texts = [
    'cronjob schedule daily 00:00 ./program.exe',
    'schtasks /create /tn "TaskName" /tr "C:\program.exe" /sc daily /st 00:00',
    'xcopy C:\Program Files (x86) E:\Program Files /E /H /K /O /X'
]

tokenizer = AutoTokenizer.from_pretrained("CyCraftAI/CmdCaliper-base")
model = AutoModel.from_pretrained("CyCraftAI/CmdCaliper-base")

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

Sentence Transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("CyCraftAI/CmdCaliper-base")
# Run inference
sentences = [
    'cronjob schedule daily 00:00 ./program.exe',
    'schtasks /create /tn "TaskName" /tr "C:\program.exe" /sc daily /st 00:00',
    'xcopy C:\Program Files (x86) E:\Program Files /E /H /K /O /X'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

📊 Metric

⚠️ Limitation

⚠️ Important Note

This model focuses exclusively on Windows command lines. Additionally, any lengthy texts will be truncated to a maximum of 512 tokens.

📄 Citation

@inproceedings{huang2024cmdcaliper,
  title={CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research},
  author={SianYao Huang, ChengLin Yang, CheYu Lin, and ChunYing Huang},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing,
  year={2024}
}