athena-trust-sdk

v1.2.0 suspicious
5.0
Medium Risk

ATHENA SDK for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows typical network activity for an SDK but lacks transparency regarding its maintainers and source code availability, raising concerns about potential supply-chain risks.

  • Sparse maintainer information
  • No associated GitHub repository
Per-check LLM notes
  • Network: The presence of network calls is typical for SDKs, especially those named with 'trust' which might imply interaction with a service.
  • Shell: No shell execution patterns detected, indicating low risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has no associated GitHub repository and the maintainer's information is sparse, which raises some concerns.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (574 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 86 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)

  • self._client = httpx.AsyncClient( base_url=self._base_url, timeout=se
  • imeout self._client = httpx.Client( base_url=self.base_url, timeout=tim
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: athena.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 athena-trust-sdk 
Develop a mini-application called 'TrustVerifier' using the Python package 'athena-trust-sdk'. This application will serve as a tool for verifying the trustworthiness of various online entities such as websites, social media profiles, and email addresses. The goal is to provide users with an easy-to-use interface to input an entity and receive a trust score based on multiple factors including but not limited to reputation, security practices, and user feedback.

The application should include the following features:
1. User Interface: A simple and intuitive web-based UI where users can enter the URL, username, or email address they wish to verify.
2. Trust Score Calculation: Utilize the 'athena-trust-sdk' to fetch and analyze data related to the entity's trustworthiness. The SDK provides methods to retrieve reputation scores, security assessments, and community feedback which should all be factored into the final trust score.
3. Visualization of Results: Display the calculated trust score alongside a brief summary of the factors contributing to the score. Use color coding or icons to visually represent the level of trust (e.g., green for high trust, yellow for medium, red for low).
4. Historical Data Tracking: Implement a feature to allow users to track changes in the trust score over time for any given entity. Store historical data locally and present it through a graph or chart within the application.
5. Security Measures: Ensure that the application securely handles user inputs and outputs, especially when dealing with sensitive information like email addresses.

To utilize the 'athena-trust-sdk', follow these steps:
- Install the package via pip: `pip install athena-trust-sdk`
- Import necessary modules from the SDK in your Python scripts.
- Use the provided functions to fetch data about the entities being verified.
- Process and aggregate this data to calculate the overall trust score.
- Integrate the SDK functionalities seamlessly with the rest of your application's logic.

This project aims to demonstrate the practical application of the 'athena-trust-sdk' in real-world scenarios, providing valuable insights into the trustworthiness of online entities.

💬 Discussion Feed

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