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---
title: LLM Threat Association Analysis
emoji: 🕸️
colorFrom: red
colorTo: purple
sdk: gradio
sdk_version: 5.32.0
app_file: app.py
pinned: false
license: mit
short_description: Can a security-tuned LLM rival STIX’s expressiveness?
---
# 🕸️ LLM Threat Association Analysis
*Visualizing Campaign-Actor-Technique relationships using Language Models*
## Features
- **Campaign-Actor Associations**: Probabilistic analysis using softmax normalization
- **Campaign-Technique Associations**: Independent binary scoring with length normalization
- **Customizable Prompt Templates**: Edit templates for different analysis scenarios
- **Interactive Heatmaps**: Matplotlib/Seaborn visualizations
- **ZeroGPU Support**: Optimized for Hugging Face Spaces GPU infrastructure
## ZeroGPU Configuration
This Space is optimized for ZeroGPU deployment with the following configuration:
### Environment Variables Required
Set these in your Space settings:
**Secret Variables:**
- `HF_TOKEN`: Your Hugging Face access token
**Regular Variables:**
- `ZEROGPU_V2=true`: Enables ZeroGPU v2
- `ZERO_GPU_PATCH_TORCH_DEVICE=1`: Enables device patching for PyTorch
### Technical Specifications
- **GPU Type**: NVIDIA H200 slice
- **Available VRAM**: 70GB per workload
- **PyTorch Version**: 2.4.0 (ZeroGPU compatible)
- **Gradio Version**: 5.29.0
## Usage
1. **Enter Campaigns**: Comma-separated list of threat campaigns
2. **Configure Prompt Templates**: Customize the language patterns used for analysis
3. **Select Actors/Techniques**: Enter relevant threat actors and techniques
4. **Generate Heatmaps**: Click buttons to create visualizations
## Installation
For local development:
```bash
pip install -r requirements.txt
python app.py
```
## Architecture
### Campaign-Actor Analysis
- Uses `P(actor | "{campaign} is conducted by")` with softmax normalization
- Results in probability distributions (sum to 1.0 per campaign)
- Shows relative likelihood of actor attribution
### Campaign-Technique Analysis
- Uses binary association scoring with length normalization
- Independent scores for each campaign-technique pair
- Accounts for phrase length bias in language models
## Model Support
Currently supports any Hugging Face transformers model. Default model is `sshleifer/tiny-gpt2` for demonstration purposes.
To use a different model, update the `MODEL_NAME` variable in `app.py`.
## References
Based on the ZeroGPU usage guide: https://huggingface.co/spaces/nyasukun/compare-security-models/blob/main/zerogpu.md
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