anolis-telemetry-export

v0.1.0 suspicious
5.0
Medium Risk

Telemetry export service for the Anolis bioreactor runtime

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple indicators of potential misuse, including network calls and shell executions that could be leveraged for unintended purposes. While not conclusively malicious, the overall risk is elevated due to the lack of clear documentation and suspicious coding practices.

  • network calls to external servers
  • shell execution capabilities
Per-check LLM notes
  • Network: Network calls suggest data transmission to external servers which may be normal if the package is designed for telemetry but could indicate data exfiltration.
  • Shell: Shell execution might be used for package functionality, but it can also introduce risks if not properly sanitized or controlled.
  • Obfuscation: The presence of gzip magic prefix suggests data compression rather than malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The package shows signs of low effort and potential lack of transparency, raising concerns about its authenticity.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_telemetry_export_service_e2e.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 110 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 6.0

Found 4 network call pattern(s)

  • "] = selector response = requests.post( f"{args.base_url.rstrip('/')}/v1/exports/signals:qu
  • manifest_response = requests.get( f"{args.base_url.rstrip('/')}/v1/exports/ma
  • try: response = requests.post( url, params={"org": config.influx.o
  • (lines) + "\n" response = requests.post( f"{influx_url}/api/v2/write", params={"org"
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ne gzip_magic_prefix = b"\x1f\x8b\x08\x00\x00\x00\x00\x00\x00\x03" monkeypatch.setattr( module, "influx_q
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • g="utf-8") process = subprocess.Popen( [ sys.executable,
✓ 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 8.0

4 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)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anolis-telemetry-export 
Develop a real-time monitoring dashboard for the Anolis bioreactor runtime using Python and the 'anolis-telemetry-export' package. This dashboard will allow users to visualize key metrics such as temperature, pH levels, dissolved oxygen, and nutrient concentrations from multiple bioreactors in a single interface. Additionally, the dashboard should provide historical data analysis capabilities, alert notifications for abnormal conditions, and integration with other systems like SCADA or MES for seamless data flow. Here are the steps and features you need to implement:

1. **Setup Environment**: Ensure you have Python installed along with 'anolis-telemetry-export'. Use Flask or Django for web development.
2. **Data Collection**: Utilize 'anolis-telemetry-export' to collect telemetry data from the Anolis bioreactor runtime. Implement a scheduled task to periodically fetch this data.
3. **Database Integration**: Store collected data in a database (e.g., SQLite, PostgreSQL). Design tables to accommodate different types of telemetry data efficiently.
4. **Real-Time Dashboard**: Develop a user-friendly dashboard using libraries like Plotly or Matplotlib for visualizing real-time and historical data. Include interactive elements for zooming, panning, and selecting specific time ranges.
5. **Historical Analysis**: Provide tools for users to analyze historical data trends over days, weeks, or months. Offer statistical summaries and predictive analytics based on past performance.
6. **Alert System**: Configure an alert system that triggers notifications (email/SMS) when certain thresholds are breached. Users should be able to set their own threshold values.
7. **System Integration**: Allow integration with external systems through APIs. Users should be able to export data in various formats (CSV, JSON).
8. **Security & User Management**: Implement basic security measures including user authentication and authorization. Restrict access based on user roles.
9. **Documentation**: Write comprehensive documentation detailing how to install, configure, and use the dashboard. Include examples and best practices.

💬 Discussion Feed

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