PyOPIA

v2.16.5 suspicious
4.0
Medium Risk

A Python Ocean Particle Image Analysis toolbox.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some level of obfuscation and shows signs of low maintenance effort, raising concerns about its integrity and security. While there is no clear evidence of malicious activity, the combination of these factors suggests potential risks that warrant further investigation.

  • Signs of obfuscation
  • Low maintenance effort
Per-check LLM notes
  • Network: The observed network calls are likely for downloading required files or models, which is common for many packages. However, the origin and integrity of the URLs should be verified.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code shows signs of obfuscation with incomplete and suspicious patterns, but it's unclear if this is intended for malicious purposes or legitimate obfuscation.
  • Credentials: No clear evidence of credential harvesting was found in the provided snippets.
  • Metadata: The package shows low maintenance effort and may indicate a less experienced or inactive maintainer, but there are no clear signs of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • r.info("Downloading....") urllib.request.urlretrieve(url, download_directory + "/silcam_database.zip"
  • et/test-data/" + filename urllib.request.urlretrieve(url, os.path.join(download_directory, filename))
  • ng example model...") urllib.request.urlretrieve(model_url, model_path) return str(model_path
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • processing. """ model.eval() try: scripted = torch.jit.script(model) ex
  • ) self.model.eval() metadata = json.loads(extra_files["metadata.json"
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: plymouth.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository sintef/pyopia appears legitimate

⚠ 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 PyOPIA 
Create a fully-functional mini-application that leverages the PyOPIA package to analyze ocean particle images. This application will serve as a tool for marine biologists and researchers to gain insights into the behavior and distribution of particles in the ocean. The app should include the following key features:

1. **Image Upload**: Users should be able to upload multiple ocean particle images.
2. **Particle Detection**: Utilize PyOPIA's particle detection algorithms to identify and count particles within each image.
3. **Data Visualization**: Implement interactive visualizations to display particle counts, sizes, and distributions across different images.
4. **Analysis Reports**: Generate comprehensive reports summarizing the analysis results, including statistical data on particle sizes and concentrations.
5. **Export Options**: Allow users to export the analysis results in various formats such as CSV, PDF, and Excel.
6. **User Interface**: Design a user-friendly interface using a Python framework like Streamlit or Flask.

The application should follow these steps:

- Initialize the project environment with necessary dependencies including PyOPIA.
- Develop a backend using PyOPIA's functionalities for image processing and analysis.
- Create a frontend that allows users to interact with the backend services through a web interface.
- Ensure that all data is securely handled and stored, adhering to privacy and security best practices.
- Test the application thoroughly to ensure accuracy and reliability of the particle analysis.

By completing this project, you'll gain hands-on experience with PyOPIA and contribute to advancing marine research.

💬 Discussion Feed

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