AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential risk, particularly concerning obfuscation practices that deviate from standard coding norms. While there's no concrete evidence of malicious intent, further scrutiny is warranted.
- Unusual obfuscation patterns
- Author has only one package listed
Per-check LLM notes
- Network: The use of session management indicates network communication which is common for packages interacting with external services like APIs.
- Shell: No shell execution patterns detected, which is expected and safe.
- Obfuscation: The observed obfuscation pattern is unusual and may indicate an attempt to hide code logic, but it could also be part of a legitimate, albeit unconventional, coding practice.
- Credentials: No direct evidence of credential harvesting was found; however, further investigation into how the package handles sensitive information is recommended.
- Metadata: The author has only one package, which might indicate a new or less active account, but no other suspicious elements were found.
Heuristic Checks
Outbound Network Calls
score 4.5
Found 3 network call pattern(s)
: self._session = requests.Session() return self def __exit__(self, exc_type, exc_self._session = aiohttp.ClientSession() self._odata = _AsyncODataClient(: self._session = aiohttp.ClientSession() return self async def __aexit__(self, exc_typ
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
config or __import__( "PowerPlatform.Dataverse.core.config", fromlist=["DataverseConfig"] ).DataverseConfig.from_env() ) self._logical_
Shell / Subprocess Execution
No shell execution patterns detected
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
Repository microsoft/PowerPlatform-DataverseClient-Python appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Microsoft Corporation License-Expression: MIT" 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 PowerPlatform-Dataverse-Client
Create a Python-based mini-application that acts as a bridge between your local system and Microsoft Dataverse. This application will allow users to perform CRUD (Create, Read, Update, Delete) operations on their Dataverse data seamlessly. Utilize the 'PowerPlatform-Dataverse-Client' Python package to interact with Dataverse efficiently. Here’s a detailed breakdown of what your application should include: 1. **Authentication**: Implement OAuth2 authentication flow to securely connect to Dataverse. Ensure that the user can provide their credentials or tokens to authenticate. 2. **Data Retrieval**: Enable the user to query specific tables in Dataverse and retrieve records based on filters provided by the user. 3. **Data Manipulation**: Allow users to create new records, update existing ones, and delete records from specified tables within Dataverse. 4. **User Interface**: Develop a simple command-line interface (CLI) or a basic web interface using Flask for interacting with the application. The CLI/web interface should be intuitive and easy to use. 5. **Error Handling**: Implement robust error handling to manage exceptions such as invalid queries, connection failures, and permission issues gracefully. 6. **Documentation**: Provide comprehensive documentation on how to set up and use the application, including setup instructions for the 'PowerPlatform-Dataverse-Client' package. 7. **Testing**: Include unit tests to ensure the reliability of the application’s core functionalities. For each feature, make sure to utilize the 'PowerPlatform-Dataverse-Client' package effectively to interact with Dataverse. Explore its capabilities to optimize your code and enhance performance.