🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits medium to high risks due to potential for arbitrary code execution and code obfuscation, despite showing no signs of credential harvesting or direct network calls.

Shell risk at 7/10 due to potential for arbitrary code execution
Obfuscation risk at 7/10 due to use of pickling/unpickling

Per-check LLM notes

Network: No network calls detected, which is generally low risk.
Shell: Detected shell execution pattern suggests potential for arbitrary code execution, indicating medium to high risk.
Obfuscation: The use of pickling and un-pickling to cache and verify object states may indicate an attempt to obfuscate code logic or hide operations.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The package shows signs of low maintenance and metadata quality, which could indicate potential issues but does not strongly suggest malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

nt must be cached" ds2 = pickle.loads(pickle.dumps(ds)) assert ds2._field_accessor is None, "a
ed = pickle.dumps(v) v2 = pickle.loads(pickled) assert v == v2 v = vec3(1, 2, 3) pickl
ed = pickle.dumps(v) v2 = pickle.loads(pickled) assert v == v2 v = vec4(1, 2, 3, 4) pi
ed = pickle.dumps(v) v2 = pickle.loads(pickled) assert v == v2 # dropping metadata header
d: RadiationFieldMetadataV1 = pickle.loads(pickled) loaded_header: RadiationFieldMetadataHeaderV1 =
sor: CartesianFieldAccessor = pickle.loads(pickled_accessor) assert isinstance(loaded_accessor, Ca

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

sion({path!r}))" result = subprocess.run([sys.executable, "-c", code], capture_output=True, text=True

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ptb.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with RadFiled3D

Your task is to develop a mini-application named 'RadiationVisualizer' that leverages the 'RadField3D' Python package to visualize and manipulate 3D radiation fields. This application will serve as a tool for researchers and engineers working with radiation data in various fields such as nuclear physics, medical imaging, and environmental studies.

Step-by-Step Instructions:
1. **Setup Environment**: Ensure your development environment includes Python and the 'RadField3D' package installed. If not already installed, you can install it via pip or from source if necessary.
2. **Data Loading**: Implement functionality within 'RadiationVisualizer' to load pre-existing 3D radiation field datasets into memory using the 'RadField3D' package. These datasets could be provided in a common format like HDF5 or custom binary formats supported by 'RadField3D'.
3. **Visualization Module**: Develop a visualization module that allows users to view the loaded 3D radiation fields. Utilize matplotlib or a similar library for rendering these fields in 3D space. The visualization should allow for interactive manipulation, such as rotating, zooming, and slicing through the 3D data.
4. **Analysis Tools**: Integrate analysis tools that leverage 'RadField3D' functionalities. For example, implement features to calculate dose distributions, find peak radiation values, and compute average radiation levels across different regions of the dataset.
5. **Export Functionality**: Add the ability to export visualizations and analysis results in formats such as PNG, PDF, or even back into HDF5 for further processing or sharing.
6. **User Interface**: Design a simple command-line interface (CLI) or a graphical user interface (GUI) using Tkinter or another suitable framework to make 'RadiationVisualizer' accessible and user-friendly.
7. **Documentation and Testing**: Write comprehensive documentation for the application, including setup instructions, usage examples, and a guide on how to contribute to the project. Additionally, create a set of test cases to ensure the reliability and accuracy of the implemented features.

Suggested Features:
- Real-time updates during visualization manipulation
- Ability to apply filters or transformations to the radiation fields
- Support for multiple datasets in one session
- Customizable color maps for better differentiation of radiation levels
- Export options for raw data alongside visual representations

By following these steps and implementing the suggested features, you'll create a versatile and powerful tool for exploring and understanding 3D radiation fields.