AI Analysis
Final verdict: SUSPICIOUS
The package has legitimate use cases but raises concerns due to incomplete author metadata and shell execution risks.
- Incomplete author metadata
- Potential misuse of shell execution
Per-check LLM notes
- Network: No network calls detected.
- Shell: Shell execution is used for version control operations which seems legitimate but could be a potential risk if misused.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's information is incomplete and the account seems new or inactive, which raises some concerns but not enough to conclusively determine malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
# release = re.sub("^v", "", os.popen("git describe").read().strip()) # The short X.Y version. # vbprocess. """ return subprocess.run(cmd, check=check, text=text, **kwargs) def git_clean() ->
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: ganil.fr>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository AdrienPlacais/LightWin 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 LightWin
Create a mini-application using the 'LightWin' Python package that simulates the maintenance process of a linear accelerator (linac) in a medical setting. This application will help medical physicists and engineers to quickly assess and compensate for cavity failures in linacs, ensuring that radiation therapy treatments can continue smoothly without significant delays. The application should have the following core functionalities: 1. **Linac Simulation**: Allow users to input details about their linac, such as the type, model, and specific cavity configurations. 2. **Cavity Failure Detection**: Simulate a cavity failure scenario based on user inputs or predefined scenarios. The application should be able to detect which cavities are failing and display this information clearly. 3. **Compensation Calculation**: Utilize the 'LightWin' package to automatically calculate the necessary compensation steps to mitigate the impact of the failed cavities. This should include adjustments in power levels, phase settings, etc., to maintain the linac's performance within acceptable parameters. 4. **Report Generation**: Generate a detailed report summarizing the detected issues, the proposed compensation actions, and any potential impacts on treatment delivery. The report should be easily exportable in PDF format. 5. **User Interface**: Develop a user-friendly graphical interface using a library like Tkinter or PyQt, allowing users to interact with the application easily. Additionally, consider adding the following advanced features: - **Scenario Saving**: Allow users to save different scenarios for future reference or testing. - **Performance Visualization**: Provide visual representations of the linac's performance before and after compensation, using charts or graphs. - **Integration with External Data Sources**: If possible, integrate the application with external databases or systems where linac performance data is stored, to automate the detection and reporting process. Your task is to design and implement this application, focusing on making it both functional and user-friendly. Make sure to thoroughly document your code and provide clear instructions for installing and running the application.