AI Analysis
Final verdict: SUSPICIOUS
The package is rated suspicious due to its network risk score and metadata concerns, despite showing no clear signs of malicious intent.
- Network risk due to external downloads
- Author with limited published packages
Per-check LLM notes
- Network: The use of requests.get with stream=True suggests the package may be downloading content from external sources, which could indicate legitimate functionality like fetching updates or resources, but also potential risks if not properly secured.
- Shell: No shell execution patterns were detected, indicating a low risk of immediate system compromise through shell commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting legitimate use without secret theft concerns.
- Metadata: The package has some minor concerns such as a non-secure link and an author with only one published package, but no clear signs of malicious intent.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
ing little by little with requests.get(url, stream=True) as response: #Stop and raise an er
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: unibocconi.it
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://doi.org/10.1038/s41586-024-07765-7
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Victor Buendia" 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 Microns-Combiner
Create a mini-application named 'DataMeld' that leverages the 'Microns-Combiner' Python package to streamline the downloading and processing of data from the mm3 data repository. This tool will be particularly useful for researchers who need to integrate and analyze complex datasets efficiently. Step 1: Design the Application Structure - Define the main functionalities that 'DataMeld' should offer, such as downloading specific datasets, processing them into a usable format, and exporting the processed data. - Decide on the user interface, whether it's command-line based or through a simple graphical interface. Step 2: Implement Core Functionality - Use the 'Microns-Combiner' package to implement functions for downloading datasets from the mm3 repository. Ensure these functions handle authentication and error checking. - Develop processing routines within 'DataMeld' to clean, normalize, and merge datasets as needed. Step 3: Enhance Usability - Add options for users to customize the processing steps, such as specifying filters or transformations during data processing. - Integrate logging and progress tracking to keep users informed about the status of their operations. Suggested Features: - Support for batch downloads of multiple datasets. - Interactive data visualization tools to explore processed data. - Export capabilities to save processed data in various formats (CSV, JSON, etc.). How to Utilize 'Microns-Combiner': - Leverage 'Microns-Combiner's functions for downloading and initial data processing to ensure seamless integration with the mm3 data. - Customize the package's existing methods or extend its functionality to meet specific needs of 'DataMeld'. - Document all interactions with 'Microns-Combiner' to maintain clarity and facilitate future updates or modifications.