aio-sensorthings

v0.1.0 suspicious
4.0
Medium Risk

A python SensorThingsAPI client and sdk

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some unusual characteristics that raise concerns about its legitimacy, particularly the lack of maintainer history and a git repository. However, there are no clear signs of malicious activity.

  • Lack of maintainer history
  • Absence of git repository
Per-check LLM notes
  • Network: The observed network call pattern is typical for an asynchronous HTTP client session, likely used for API interaction.
  • Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
  • Obfuscation: The import statement is obfuscated but does not indicate malicious intent; it's likely for code organization or protection.
  • Credentials: No credential harvesting patterns detected.
  • Metadata: The package shows several red flags including lack of maintainer history and git repository, which raises concerns about its legitimacy.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

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

Some documentation present

  • Detailed PyPI description (6363 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 206 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 1.5

Found 1 network call pattern(s)

  • ient: self._session = aiohttp.ClientSession( auth=self._auth(), timeout=self._ti
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • r.add_span_processor( __import__( "opentelemetry.sdk.trace.export", fromlist=["SimpleSpanProcessor"], ).SimpleSpanProcessor(span_exporter) ) tracer = trace
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: gv.hamburg.de>

  • Very short email domain: gv.hamburg.de>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 aio-sensorthings 
Create a real-time environmental monitoring dashboard using the Python package 'aio-sensorthings'. This mini-application will allow users to visualize data from various environmental sensors such as temperature, humidity, air quality, and more. The app should have the following functionalities:

1. **Data Retrieval**: Utilize 'aio-sensorthings' to fetch live sensor data from a SensorThings API endpoint. Ensure the application can handle asynchronous requests efficiently.
2. **Data Visualization**: Implement a user-friendly interface where users can see real-time updates of sensor readings. Consider using libraries like Matplotlib or Plotly for plotting graphs and charts.
3. **Customization Options**: Allow users to select which types of sensors they want to monitor and display on their dashboard. Users should also be able to choose the time interval for data refresh.
4. **Alert System**: Integrate an alert system that notifies users via email or SMS if any sensor reading exceeds predefined thresholds.
5. **Storage and Analysis**: Optionally, store the fetched sensor data into a local database (e.g., SQLite) for historical analysis. Provide basic analysis tools within the app to help users understand trends over time.
6. **User Authentication**: Implement a simple login system to ensure only authorized users can access the dashboard and configure settings.

The 'aio-sensorthings' package will be crucial in handling all interactions with the SensorThings API, ensuring efficient data fetching and processing. Your task is to design and implement this application, detailing each step from setting up the environment to deploying the final product.

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