avtdl

v2.11.3 suspicious
5.0
Medium Risk

Monitoring and automation tool for Youtube and other streaming platforms

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential obfuscation techniques that could hide malicious code, despite having low risks in other categories like shell execution and credential harvesting.

  • Potential obfuscation techniques
  • Single-package author
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession suggests the package performs network requests, which is common but should be reviewed for destination and purpose.
  • Shell: No shell execution patterns detected, indicating low risk.
  • Obfuscation: The observed patterns suggest potential obfuscation techniques that could be used to hide malicious code or logic.
  • 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 no other suspicious flags are present.

📦 Package Quality Overall: Low (4.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/15532th/avtdl
  • Detailed PyPI description (1562 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

  • 643 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in 15532th/avtdl
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • s else None session = aiohttp.ClientSession(cookie_jar=cookies, headers=headers) self.session =
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • split('.') payload_json = base64.b64decode(payload.encode('utf-8') + b'====') payload_dict = json.l
  • (module_name) __import__(module_name, fromlist=m.__all__) except Exception: cls.logger.ex
  • (data: bytes): return pickle.loads(data) @classmethod def restore(cls, Model: Type[Dat
✓ 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 15532th/avtdl appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "15532th" 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 avtdl 
Develop a real-time video monitoring and alerting system using the 'avtdl' Python package. This system will allow users to monitor specific YouTube channels or videos for new uploads, changes in video titles/descriptions, and even live stream status. Additionally, the app should be able to send notifications via email or SMS when certain conditions are met.

Step 1: Set up the environment
- Install Python and necessary packages including 'avtdl'.
- Configure your development environment to use virtual environments.

Step 2: Design the User Interface
- Create a simple web interface using Flask or Django where users can input channel IDs, video IDs, or keywords they wish to monitor.
- Implement forms for users to specify conditions under which they want alerts (e.g., video title contains specific words).

Step 3: Develop Monitoring Logic
- Use 'avtdl' to periodically fetch data from YouTube API regarding the specified channels/videos.
- Compare fetched data against previously stored data to detect any changes.

Step 4: Notification System
- Integrate SMTP for sending emails as alerts.
- Optionally, integrate Twilio for sending SMS alerts.

Step 5: Testing and Deployment
- Test the application thoroughly to ensure it correctly identifies changes and sends appropriate alerts.
- Deploy the application on a cloud service like AWS or Heroku.

Suggested Features:
- Real-time updates with WebSocket integration for immediate alerts.
- Customizable alert thresholds (e.g., only alert if video length exceeds X minutes).
- Support for multiple monitoring platforms beyond YouTube.
- Historical data tracking and analytics dashboard.

💬 Discussion Feed

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