axor-telemetry

v0.4.0 suspicious
4.0
Medium Risk

Anonymous telemetry pipeline for axor-core: MinHash embedder, local/HTTP sinks, opt-in consent CLI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse, particularly due to its low activity repository and sparse maintainer information, combined with network and shell execution risks.

  • Network risk due to unverified endpoints
  • Shell risk from subprocess execution
  • Sparse repository activity and maintainer information
Per-check LLM notes
  • Network: The network calls could be part of telemetry reporting but require verification of the endpoint's legitimacy and purpose.
  • Shell: Executing the package via subprocess may be intended for internal operations or help commands, but it should be reviewed to ensure no unintended actions are being performed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The repository has no activity and the maintainer information is sparse, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

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

Some documentation present

  • Detailed PyPI description (2383 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

  • 42 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in Bucha11/axor-telemetry
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • elf._auth_token req = urllib.request.Request( self._endpoint, data=payloa
  • try: with urllib.request.urlopen(req, timeout=self._timeout) as resp:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • subcommands.""" result = subprocess.run( [sys.executable, "-m", "axor_telemetry", "--help"],
  • bin:/bin", } result = subprocess.run( [sys.executable, "-m", "axor_telemetry", "status"],
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 axor-telemetry 
Create a mini-application named 'TelemetryTracker' using Python that leverages the 'axor-telemetry' package to demonstrate its capabilities in collecting and transmitting anonymous telemetry data. This application will serve as a proof of concept for developers interested in understanding how to implement telemetry in their projects without compromising user privacy. Here’s a detailed plan on how to approach building this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed, then install the 'axor-telemetry' package via pip.
2. **Design Application Structure**: Design a simple yet effective structure for the TelemetryTracker app. It should include modules for data collection, data processing, and data transmission.
3. **Data Collection Module**: Implement a module that uses the MinHash embedder from 'axor-telemetry' to collect anonymous data. This could involve simulating user interactions or gathering system metrics.
4. **Consent Management**: Integrate the opt-in consent CLI provided by 'axor-telemetry'. This ensures that users can control whether their data is collected and transmitted.
5. **Local Storage**: Develop a feature where collected telemetry data is stored locally before being sent out. Use the local sink capability of 'axor-telemetry' for this purpose.
6. **HTTP Data Transmission**: Once consent is given and data is collected, implement functionality to send this data to a remote server using the HTTP sink provided by 'axor-telemetry'.
7. **User Interface**: Optionally, create a simple command-line interface (CLI) for users to interact with the TelemetryTracker. They should be able to start and stop data collection, view local storage contents, and manage consent settings through this interface.
8. **Testing and Documentation**: Finally, thoroughly test the application to ensure all components work as expected. Document the setup process, usage instructions, and any configurations needed for running TelemetryTracker successfully.

This project aims to showcase the flexibility and ease of use of the 'axor-telemetry' package while emphasizing the importance of respecting user privacy in data collection practices.

💬 Discussion Feed

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