attenlabs-sas

v0.2.1 suspicious
4.0
Medium Risk

Python SDK for AttentionLabs real-time attention detection.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to its network interactions and lack of associated metadata like a GitHub repository. Further verification is recommended before use.

  • Network risk indicated by unverified external service interaction
  • Lack of linked GitHub repository suggesting possible unfamiliarity or suspicious activity
Per-check LLM notes
  • Network: The observed network calls may be legitimate if the package is designed to interact with external services, but further investigation is needed to confirm its purpose and destination.
  • Shell: No shell execution patterns detected, which is normal and does not indicate immediate risk.
  • Obfuscation: The observed pattern is likely for data encoding or decoding purposes rather than obfuscation to hide malicious code.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The maintainer has only one package and no linked GitHub repository, which may indicate a less experienced or potentially suspicious account.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (9034 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

  • 27 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • f.token else {} req = urllib.request.Request( allocate_url, method="POST", headers=he
  • try: with urllib.request.urlopen(req, timeout=BROKER_ALLOCATE_TIMEOUT_S) as resp:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • (0, dtype=np.int16) raw = base64.b64decode(b64) return np.frombuffer(raw, dtype=np.int16).copy()
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Attention Labs" 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 attenlabs-sas 
Create a real-time attention monitoring tool using the 'attenlabs-sas' Python package. This application will allow users to monitor their focus levels during specific activities such as studying, working, or watching videos. The tool should have a user-friendly interface that displays real-time attention scores, provides historical data visualization, and offers tips based on the detected attention levels.

Steps to Develop the Application:
1. Set up a Python environment and install the 'attenlabs-sas' package.
2. Design a simple graphical user interface (GUI) using a library like Tkinter or PyQt.
3. Integrate the 'attenlabs-sas' package to continuously capture and process real-time attention data.
4. Display the attention score in real-time within the GUI, updating every second.
5. Implement a feature to log the attention scores over time and store them locally.
6. Add a chart or graph to visualize the historical attention data.
7. Incorporate a tips module that suggests ways to improve focus based on the current attention level.
8. Test the application thoroughly to ensure smooth operation and accurate data collection.
9. Document the setup and usage instructions for end-users.

Suggested Features:
- Real-time attention score display
- Historical attention data logging and visualization
- Tips for improving focus based on attention levels
- Customizable alert settings to notify users when attention drops below a certain threshold
- Export functionality to save attention data to CSV files

How to Utilize 'attenlabs-sas':
- Use 'attenlabs-sas' to initialize the attention detection process.
- Continuously fetch and process attention data from the package in real-time.
- Pass the processed data to your GUI for real-time display and historical logging.
- Leverage the package's capabilities to enhance the accuracy and reliability of the attention monitoring.

💬 Discussion Feed

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