AI Analysis
Final verdict: SAFE
The package shows low risk indicators across all categories, with no signs of malicious activity. The minor obfuscation noted is likely benign.
- Low network and shell execution risks
- Minimal obfuscation observed, likely benign
- No credential harvesting detected
Per-check LLM notes
- Network: No network calls detected, which is not unusual if the package is designed to work offline or with local resources.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
- Obfuscation: The observed patterns are likely related to base64 decoding and not indicative of malicious obfuscation but could be used for data serialization.
- Credentials: No patterns indicating credential harvesting were detected.
- Metadata: The author Microsoft has only one package, which might indicate a new or less active account.
Package Quality Overall: Low (4.6/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4548 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
337 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
25 unique contributor(s) across 100 commits in microsoft/azure-quantum-pythonActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
return attr return bytes(base64.b64decode(attr)) def _deserialize_bytes_base64(attr): if isie("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isin
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/azure-quantum-python appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Microsoft" 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 azure-quantum
Create a Python-based mini-application that leverages the Azure Quantum service to solve complex optimization problems. This application will allow users to input their own problem specifications and then use Azure Quantum to find the optimal solution. The application should include the following features: 1. User Interface: Develop a simple command-line interface (CLI) for users to interact with the application. 2. Problem Input: Allow users to specify the type of optimization problem they wish to solve (e.g., traveling salesman problem, maximum cut problem). 3. Parameter Specification: Enable users to define the parameters of the problem, such as number of nodes, edge weights, etc. 4. Job Submission: Use the 'azure-quantum' Python package to submit the problem specification to Azure Quantum. 5. Solution Retrieval: Once the job is completed, retrieve the solution from Azure Quantum and display it to the user. 6. Performance Metrics: Provide users with performance metrics such as computation time, cost, and quality of the solution. 7. Documentation: Include comprehensive documentation explaining how to install the application, set up Azure Quantum, and use the CLI effectively. The 'azure-quantum' package will be utilized throughout the project for interfacing with Azure Quantum services, submitting jobs, and retrieving results. It will be crucial in translating the user-defined problem into a format that Azure Quantum can understand and process.
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