AI Analysis
Final verdict: SAFE
The package is deemed safe with a moderate risk score due to potential shell execution, but lacks other signs of malicious activity such as obfuscation or credential harvesting.
- Shell execution detected, requiring further investigation.
- Maintainer's author name is missing, indicating potential inactivity.
Per-check LLM notes
- Network: No network calls detected, which is low risk.
- Shell: Shell execution detected may indicate the package performs system tasks, but requires further investigation to ensure it's not being used maliciously.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting no risk of secret theft.
- Metadata: The maintainer's author name is missing and appears to be new or inactive, which raises some concern but not enough to conclude malicious intent.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
returncode).""" result = subprocess.run( LZG + list(args), capture_output=True, text
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
Repository MuteJester/LZGraphs appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 LZGraphs
Create a Python-based mini-application named 'Immunograph' that leverages the LZGraphs package to analyze and visualize immune receptor repertoires. This application will enable researchers to upload their sequence data, generate high-performance LZ76 compression graphs, and perform various analyses on these graphs to understand the diversity and specificity of immune responses. Step 1: Set up the project structure and install necessary dependencies including LZGraphs. Step 2: Design a user-friendly command-line interface (CLI) where users can: - Upload FASTA formatted sequence files. - Select from a variety of graph generation parameters tailored for different types of immune receptors. - View basic statistics about the generated graphs such as node count, edge count, etc. Step 3: Implement functions within Immunograph to process the uploaded sequences using LZGraphs to create LZ76 compression graphs. Ensure that the application provides options to optimize graph generation based on computational resources available. Step 4: Develop analytical tools within Immunograph to calculate key metrics from the LZ76 graphs such as: - Diversity index (e.g., Simpson's Index) - Specificity score - Clustering coefficients These metrics will help researchers gauge the complexity and specificity of the immune response captured in the dataset. Step 5: Integrate visualization capabilities into Immunograph allowing users to plot and explore their LZ76 graphs interactively. Visualization options should include: - Graph layouts - Highlighting specific nodes or edges - Interactive tooltips providing additional information about each node or edge Step 6: Add documentation and examples to guide users through the setup and usage of Immunograph. Include tutorials that demonstrate how to use Immunograph for common research scenarios involving immune receptor analysis. By completing these steps, you'll have built a powerful yet accessible tool for immunology researchers looking to leverage advanced graph theory concepts to study immune receptor repertoires.