LLMPot: Automated LLM-based Industrial Protocol and Physical Process Emulation for ICS Honeypots
Paper • 2405.05999 • Published
Text Generation
Transformers
Safetensors
PyTorch
English
t5
text2text-generation
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("cv43/llmpot")
model = AutoModelForSeq2SeqLM.from_pretrained("cv43/llmpot")Quick Links
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model
This is a sample finetuned model produced under LLMPot research project and explained further in the related research manuscript.
How to Use
This model is a fine-tuned version of google/byt5-small for Modbus protocol emulation.
Make sure you have transformers and torch installed:
pip install transformers torch
Load the model and run a single inference.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("cv43/llmpot")
model = AutoModelForSeq2SeqLM.from_pretrained("cv43/llmpot")
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, framework="pt")
request = "02b10000000b00100000000204ffffffff"
result = pipe(request)
print(f"Request: {request}, Response: {result[0]['generated_text']}")
Otherwise you may use our Space application where the model is running on the cloud.
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Model tree for cv43/llmpot
Base model
google/byt5-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cv43/llmpot")