🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk and shell execution, which require further scrutiny to confirm they do not serve malicious purposes. Additionally, the metadata suggests potential issues with the package's provenance.

High obfuscation risk
Potential shell execution for unauthorized actions
Lack of maintainer information

Per-check LLM notes

Network: No network calls detected, which is normal and not suspicious.
Shell: Shell execution detected, possibly for installation purposes, but requires further investigation to ensure it does not perform unauthorized actions.
Obfuscation: The use of dynamic code execution with compiled source code may indicate an attempt to bypass analysis or hide functionality.
Credentials: No clear signs of credential harvesting or secret handling observed.
Metadata: The package shows some red flags, particularly the lack of maintainer information and a single package on PyPI, which could indicate a new or inactive account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

tra_cmd=extra_cmd) code = compile(txt, setup_py, "exec") exec(code, {"SDist": SDist}) """Simple script for re

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

cmd += fcommand subprocess.run(cmd, check=True, cwd=DIR, stdout=sys.stdout, stderr=sys.stde
t, stderr=sys.stderr) subprocess.run( ["cmake", "--install", tmpdir], che

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 CLUEstering

Create a Python-based mini-application that leverages the 'CLUEstering' package to perform high-performance density-based weighted clustering on a dataset of heterogeneous data types. This application will serve as a tool for researchers and data scientists who need to analyze complex datasets with varying attributes. Here’s a detailed breakdown of what your application should accomplish:

1. **Data Ingestion**: Develop a feature that allows users to upload their datasets. Support various file formats such as CSV, Excel, and JSON. Ensure the application can handle datasets containing numerical, categorical, and textual data.

2. **Preprocessing**: Implement basic preprocessing steps including handling missing values, normalization, and encoding categorical variables if necessary. This ensures the data is ready for clustering.

3. **Clustering Interface**: Design an intuitive interface where users can select the type of clustering they want to perform using the 'CLUEstering' package. Users should be able to adjust parameters like epsilon (ε), minimum points (MinPts), and other relevant settings depending on the clustering algorithm chosen.

4. **Visualization**: After clustering, provide visual representations of the clusters. For numerical data, use scatter plots; for categorical data, consider bar charts or pie charts. Ensure these visualizations are interactive, allowing users to explore cluster characteristics more deeply.

5. **Cluster Analysis**: Offer functionalities to analyze each cluster individually. This includes calculating cluster centroids, identifying outliers, and providing statistical summaries of each cluster. Users should also be able to export these analyses in a readable format like PDF or CSV.

6. **Integration with Web Interface**: Build a web interface using Flask or Django where users can interact with the application directly from their browser. This makes the tool accessible without needing to install any software locally.

7. **Documentation and User Guide**: Provide comprehensive documentation detailing how to use the application effectively, including examples of different types of datasets and their clustering results. Also, include a guide on how to interpret the clustering outcomes.

By following these guidelines, you'll create a powerful yet user-friendly tool that demonstrates the capabilities of 'CLUEstering' while offering practical value to its users.