analygo-identity

v0.7.2 suspicious
6.0
Medium Risk

Canonical identity types and validators for Analygo services

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential obfuscation and questionable metadata, though there's no concrete evidence of credential theft or supply-chain attack.

  • Moderate obfuscation risk
  • Questionable metadata with missing author information and inactive maintainer
Per-check LLM notes
  • Obfuscation: The obfuscation pattern is suspicious but not conclusively malicious; it could be an attempt to evade detection or analysis.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The package has some red flags, including a missing author name and a new/inactive maintainer account, but no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. test_observability.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

  • 54 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 9.0

Found 6 network call pattern(s)

  • JWKS try: resp = httpx.get(jwks_url, timeout=10.0) resp.raise_for_status()
  • ncClient.""" client = httpx.AsyncClient() install_boundary_logging(client, caller_name="reg
  • callable.""" client = httpx.AsyncClient() install_boundary_logging(client, caller_name="cal
  • ort asyncio client = httpx.AsyncClient() install_boundary_logging(client, caller_name="test
  • ration.""" client_a = httpx.AsyncClient() client_b = httpx.AsyncClient() install_bo
  • ncClient() client_b = httpx.AsyncClient() install_boundary_logging(client_a, caller_name="s
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • inue logger = __import__("logging").getLogger(__name__) logger.exception("Onboa
✓ 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: analygo.com>

✓ 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 analygo-identity 
Develop a mini-application named 'Identity Validator' using Python and the 'analygo-identity' package. This application will serve as a tool for validating user identities against predefined rules and standards set by Analygo services. The app should have a simple command-line interface (CLI) for ease of use.

Key Features:
1. User Input: Allow users to input various identity-related data such as usernames, emails, phone numbers, etc.
2. Validation Rules: Implement validation rules provided by the 'analygo-identity' package to ensure the inputted data meets the required format and criteria for Analygo services.
3. Feedback System: Provide clear feedback to the user regarding whether their input passes or fails the validation tests. Include suggestions for corrections if the validation fails.
4. Data Persistence: Optionally, allow users to save successful validations into a local database or file for future reference.
5. Help Menu: Include a help menu accessible via a command (e.g., --help), which explains the usage of the application and provides examples of valid inputs.

Steps to Build:
1. Install the 'analygo-identity' package using pip.
2. Create a main script file (e.g., 'main.py') that imports necessary classes and functions from 'analygo-identity'.
3. Define a function to handle user inputs and another to validate these inputs using the 'analygo-identity' package.
4. Implement logic to display validation results and suggestions for correction if needed.
5. Add functionality to save successful validations, if chosen as a feature.
6. Develop a help menu system that guides users on how to interact with the application.
7. Test the application thoroughly with different types of input data to ensure it works correctly under various scenarios.
8. Package the application into a distributable form (e.g., a .exe file for Windows, or a .app bundle for macOS).

Utilizing 'analygo-identity':
The 'analygo-identity' package will be crucial for defining and applying the validation rules. It will provide pre-built validators for common identity types such as email addresses, usernames, phone numbers, etc. These validators will be used directly in your application to ensure that the user-provided identities meet the required standards before they are considered valid.