LabelCraft

v2.1.5 suspicious
4.0
Medium Risk

LabelCraft - A modern graphical image annotation tool based on labelImg

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious intent, but the metadata risk and shell execution risks are noteworthy, suggesting potential vulnerabilities.

  • Metadata risk due to new or inactive repository and limited maintainer history.
  • Potential shell injection risk from unsanitized commands.
Per-check LLM notes
  • Network: No network calls were detected that would suggest malicious activity.
  • Shell: The shell execution patterns observed are typical for packaging and deploying Python packages to PyPI, but may pose some risk if the commands are not properly sanitized.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The repository is new or inactive, and the maintainer has limited history with PyPI, indicating potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • rsal) distribution…') os.system( '{0} setup.py sdist bdist_wheel --universal'.fo
  • to PyPI via Twine…') os.system('twine upload dist/*') self.status('Pushing git tag
  • ('Pushing git tags…') os.system('git tag -d v{0}'.format(about['__version__'])) os.s
  • bout['__version__'])) os.system('git tag v{0}'.format(about['__version__'])) # os.sy
  • ut['__version__'])) # os.system('git push --tags') sys.exit() setup( app=APP
  • t subprocess result = subprocess.run( ['dconf', 'read', '/org/gnome/desktop/interface
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "LabelCraft Contributors" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with LabelCraft 
Create a Python-based mini-app named 'AnnotatePro' that leverages the 'LabelCraft' package to streamline the process of annotating images for machine learning datasets. This app will provide users with an intuitive interface to draw bounding boxes around objects of interest within images, label these objects, and save annotations in a structured format. Here are the key steps and features to include in your development process:

1. **Setup Environment**: Begin by setting up a virtual environment for Python 3. Ensure you install the necessary packages including LabelCraft, PyQt5, and any other dependencies.

2. **User Interface Design**: Utilize LabelCraft's graphical capabilities to design a user-friendly interface where users can upload images and start annotating them. The interface should allow users to zoom in/out, pan across the image, and toggle between different labeling tools.

3. **Annotation Tools**: Implement at least three types of annotation tools: bounding box, polygon, and point. Each tool should have its own shortcut keys for quick access.

4. **Label Management**: Allow users to create, edit, and delete labels as needed. Store these labels in a database or file for easy reference during future sessions.

5. **Saving Annotations**: Integrate functionality to save annotations in popular formats such as PASCAL VOC XML or COCO JSON. Users should be able to specify the output directory and filename.

6. **Batch Processing**: Extend the app to support batch processing of multiple images. Users should be able to select a folder containing images and annotate all images within it sequentially.

7. **Help and Documentation**: Provide comprehensive help documentation within the app explaining how to use each feature effectively. Include tooltips and contextual help messages.

8. **Testing and Validation**: Before finalizing the app, ensure thorough testing of all features. Validate the accuracy of saved annotations against manually checked examples.

By following these steps and incorporating the above features, you'll develop a robust, user-friendly annotation tool that significantly simplifies the task of preparing annotated datasets for machine learning projects.