azure-ai-transcription

v1.0.0 suspicious
4.0
Medium Risk

Microsoft Corporation Azure AI Transcription Client Library for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, particularly due to the incomplete metadata and use of base64 encoding, though these alone do not confirm malicious intent.

  • Incomplete metadata and author information.
  • Use of base64 encoding and decoding functions.
Per-check LLM notes
  • Network: No network calls are detected, which is unusual but not necessarily indicative of malicious activity; it depends on the package's intended functionality.
  • Shell: No shell executions are detected, which aligns with expected behavior for a legitimate package focused on Azure AI transcription.
  • Obfuscation: The base64 encoding and decoding functions are likely used for data serialization rather than obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has a new or inactive account with incomplete author information, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

  • Test runner config found: conftest.py
  • 15 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (18067 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 125 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-python
  • Active 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 isinsta
  • ce("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isinstan
  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # coding=u
✓ 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> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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-transcription 
Create a real-time transcription app using the 'azure-ai-transcription' Python package. This application will allow users to record audio through their device's microphone and transcribe it into text in real-time. The app should have a clean and intuitive user interface, displaying the transcribed text as the user speaks.

Key Features:
1. Real-time transcription: As the user speaks, the app should display the transcribed text on the screen.
2. Save Transcriptions: Users should be able to save their transcriptions to a local file or upload them to a cloud storage service like Azure Blob Storage.
3. Multiple Languages Support: The app should support multiple languages for transcription, allowing users to select their preferred language from a dropdown menu.
4. Export Options: Provide options for exporting the transcription data in various formats such as .txt, .docx, and PDF.
5. Error Handling: Implement error handling to manage issues such as network failures, API rate limits, and invalid input gracefully.

Steps to Build the Application:
1. Set up your development environment with Python and install the 'azure-ai-transcription' package.
2. Design the user interface using a Python GUI framework like PyQt or Tkinter.
3. Integrate the 'azure-ai-transcription' package to handle the audio streaming and transcription process.
4. Implement functionality to start and stop recording audio.
5. Display the transcribed text in real-time within the UI.
6. Add features to save and export the transcriptions.
7. Test the application thoroughly across different scenarios and languages.
8. Deploy the application for users to access via a desktop application or web interface.

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