eyeballer
by BishopFox
Eyeballer uses a convolutional neural network to analyze pentest screenshots and identify potentially vulnerable or interesting web targets.
Convolutional neural network for analyzing pentest screenshots
Primary Use Case
Eyeballer is designed for penetration testers and red teamers conducting large-scale network assessments who need to quickly sift through thousands of web screenshots to find valuable targets. By classifying screenshots into categories like login pages, old-looking sites, and parked domains, it helps prioritize attack surfaces efficiently.
- Uses convolutional neural networks to classify pentest screenshots
- Identifies valuable web targets such as login pages and web applications
- Filters out uninteresting pages like custom 404s and parked domains
- Supports integration with popular screenshot tools like EyeWitness and GoWitness
- Provides pretrained model weights for immediate use
- Offers GPU support for faster processing
- Includes labeled training data for custom model training
- Accessible via CLI and web service
Installation
- Install required Python packages with `sudo pip3 install -r requirements.txt`
- For GPU support, install packages with `sudo pip3 install -r requirements-gpu.txt`
- Download pretrained weights from the GitHub releases section
- Download training data from https://www.kaggle.com/altf42600/pentest-screensots
- Place the `images/` folder and `labels.csv` file into the root Eyeballer directory
Usage
>_ sudo pip3 install -r requirements.txtInstalls the necessary Python dependencies for CPU-only usage
>_ sudo pip3 install -r requirements-gpu.txtInstalls the necessary Python dependencies for GPU support
- Integrate Eyeballer with automated screenshot tools like EyeWitness or GoWitness to streamline target prioritization during large-scale red team engagements.
- Leverage pretrained models to rapidly classify web targets, reducing manual triage time and focusing exploitation efforts on high-value assets.
- Use Eyeballer's GPU support to accelerate processing in time-sensitive operations or continuous pentesting pipelines.
- Incorporate Eyeballer outputs into purple team exercises to improve detection rules for web-based reconnaissance and exploitation activities.
- Extend Eyeballer with custom training data to adapt classification models to specific organizational web environments or threat landscapes.
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