AI Analysis
Final verdict: SUSPICIOUS
The package is flagged as suspicious due to its usage of shell commands, which could potentially execute arbitrary code. However, the lack of network calls, obfuscation, and credential harvesting reduces the overall risk.
- Shell risk due to usage of shell commands
- Low metadata engagement
Per-check LLM notes
- Network: No network calls detected, which is normal and expected.
- Shell: The use of shell commands and subprocesses can be legitimate but raises suspicion due to potential execution of arbitrary commands.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package and lacks PyPI classifiers, suggesting low engagement or effort.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 6.0
Found 3 shell execution pattern(s)
ral", "higher", "lower"] os.system(f"mkdir -p {here / 'results'}") for i in range(3):try: version = subprocess.check_output( ["git", "describe", "--tags", "--dirty", "-ust git process = subprocess.Popen([command] + args, cwd=cwd, env=env,
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
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 "Niccolò Laurenti" 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 adani
Your task is to develop a Python-based mini-application called 'NuclearCoefficientsExplorer'. This tool will leverage the 'adani' package to compute approximate DIS (Deep Inelastic Scattering) N3LO (Next-to-Next-to-Next-to-Leading Order) coefficients, which are crucial for advanced nuclear physics research. The application should be user-friendly, allowing researchers to input necessary parameters and receive computed coefficients as output. Key Features: 1. User Interface: Design a simple command-line interface where users can enter required parameters such as energy levels, scattering angles, etc. 2. Parameter Validation: Implement validation checks to ensure all inputs are within acceptable ranges and formats. 3. Coefficient Calculation: Utilize the 'adani' package to calculate the approximate DIS N3LO coefficients based on user inputs. 4. Output Display: Present the calculated coefficients clearly and accurately, along with any relevant metadata. 5. Documentation: Provide comprehensive documentation explaining how to use the application, including examples of valid input and expected output. 6. Error Handling: Ensure robust error handling mechanisms are in place to guide users through common mistakes or issues. 7. Extensibility: Design the application to be easily extensible, allowing for future updates or integration with other packages. Steps to Build: 1. Install the 'adani' package via pip or from source if needed. 2. Set up a virtual environment for your project to manage dependencies. 3. Create a Python script that initializes the user interface. 4. Integrate the 'adani' package into your script for coefficient calculation. 5. Develop parameter validation logic to ensure correct input processing. 6. Implement the main functionality to calculate and display coefficients. 7. Add error handling and documentation to enhance usability. 8. Test the application thoroughly with various inputs to ensure reliability. 9. Package your application for easy distribution and installation. By following these steps and incorporating the specified features, you'll create a valuable tool for researchers working with DIS N3LO coefficients.