AI Analysis
Final verdict: SAFE
The package shows minimal risks across all categories except for a moderate obfuscation risk, which is likely benign given the context. There's no indication of a supply-chain attack.
- moderate obfuscation risk
- author has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: The base64 decoding patterns may indicate an attempt to obfuscate code, but could also be part of normal functionality for handling encoded data.
- Credentials: No clear signs of credential harvesting detected.
- Metadata: The author has only one package, which may indicate a new or less active account but does not necessarily suggest malintent.
Package Quality Overall: Medium (5.4/10)
○ 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 (61789 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project312 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-pythonActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 8.0
Found 4 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 isinreturn attr return bytes(base64.b64decode(attr)) def _deserialize_bytes_base64(attr): if isinstace("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isinstan
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: microsoft.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Azure/azure-sdk-for-python appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Microsoft Corporation" 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-ai-evaluation
Create a Python-based mini-application that leverages the 'azure-ai-evaluation' library to evaluate and compare the performance of different machine learning models on a given dataset. This application should serve as a tool for data scientists and machine learning engineers to quickly assess model accuracy, precision, recall, F1 score, and other relevant metrics across various datasets and models. Step-by-Step Guide: 1. Setup your development environment with Python and install necessary packages including azure-ai-evaluation, pandas, scikit-learn, and matplotlib. 2. Import sample datasets from sklearn.datasets or load your own datasets using pandas. 3. Preprocess the data as needed, including handling missing values, scaling features, and splitting the dataset into training and testing sets. 4. Implement a function to load and initialize multiple machine learning models from scikit-learn (e.g., Logistic Regression, SVM, Random Forest). 5. Use the azure-ai-evaluation library to evaluate each model on the test dataset. Focus on implementing key evaluation functions provided by the library such as accuracy_score, precision_score, recall_score, and f1_score. 6. Visualize the results using matplotlib or any other plotting library of your choice. Create graphs that compare the performance metrics of each model. 7. Add functionality to save the evaluation results to a CSV file for future reference. 8. Finally, create a user-friendly command-line interface (CLI) that allows users to select which datasets and models they want to evaluate. Suggested Features: - Support for loading custom datasets. - Ability to add more machine learning models in the future without changing much code. - Option to visualize ROC curves and confusion matrices. - Detailed documentation and comments in the code to make it easy for others to understand and extend.
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