AI Analysis
Final verdict: SUSPICIOUS
The package shows potential risks due to its capability to execute shell commands, which could be exploited for malicious purposes. However, it lacks other typical indicators of malicious intent.
- Shell execution patterns detected
- Non-HTTPS links in metadata
Per-check LLM notes
- Network: No network calls detected, which is normal and not suspicious.
- Shell: Shell execution patterns detected may indicate the package is intended to run system commands, but could also suggest potential for executing arbitrary code, which is risky.
- 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 has only one package, and there are non-HTTPS links, but no clear signs of malicious activity or typosquatting.
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)
], shell=True, ) print(co(["make", "-j3"]))ersion__)"'], shell=True, stderr=sp.STDOUT, )
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
Suspicious Page Links
score 6.0
Found 3 suspicious link(s) on the package page
Non-HTTPS external link: http://www.opensourcebrain.org/projects/fitzhugh-nagumo-fitzhugh-1969Non-HTTPS external link: http://www.opensourcebrain.org/projects/acnet2Non-HTTPS external link: http://www.opensourcebrain.org/projects/sbmlshowcase
Git Repository History
Repository OpenSourceBrain/osb-model-validation appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Boris Marin, Padraig Gleeson" 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 OSBModelValidation
Create a mini-application named 'BrainModelValidator' using Python that leverages the 'OSBModelValidation' package to validate various brain models against specific criteria. This application will serve as a tool for researchers and neuroscientists to ensure the accuracy and reliability of their brain models before proceeding with further analysis or publication. Step 1: Define the core functionalities of the application. The app should allow users to upload a brain model file (e.g., in .json or .csv format), specify validation criteria (such as structural integrity, connectivity patterns, and functional consistency), and run the validation process. Step 2: Implement a user-friendly interface. While the initial version can be command-line based, consider adding a graphical user interface (GUI) using libraries like PyQt or Tkinter in future iterations. Step 3: Integrate the 'OSBModelValidation' package. Utilize its functions to perform the actual validation checks on the uploaded brain model. Ensure that the validation process is modular, allowing for easy addition or removal of validation criteria. Step 4: Provide detailed output. After running the validation, the application should generate a comprehensive report detailing any issues found, suggestions for improvement, and a pass/fail status for each criterion specified by the user. Suggested Features: - Support for multiple input formats (JSON, CSV, etc.) - Ability to customize validation criteria via a configuration file - Option to save the validation report as a PDF or HTML document - Integration with cloud storage services for uploading and downloading models - Real-time feedback during the validation process The 'OSBModelValidation' package is utilized throughout the application to perform the core validation tasks. Users will select validation criteria from a predefined list or custom configurations, which will then be passed to the appropriate functions within the 'OSBModelValidation' package to check the brain model's compliance.