allyanonimiser

v3.5.0 suspicious
4.0
Medium Risk

Australian-focused PII detection and anonymization for the insurance industry

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network and shell activities, but the metadata indicates a potentially new or inactive maintainer without detailed author information, raising suspicion.

  • Low network and shell risk
  • New or inactive maintainer with insufficient author details
Per-check LLM notes
  • Network: No network calls suggest the package does not engage in external communications, which is normal unless specific functionality requires it.
  • Shell: No shell execution patterns indicate the package does not execute system commands, reducing potential for harmful actions.
  • Metadata: The maintainer has a new or inactive account and lacks author details, raising some concerns but not definitive proof of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 27 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 27 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/srepho/Allyanonimiser#readme
  • Detailed PyPI description (46941 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

  • Type checker (mypy / pyright / pytype) referenced in project
  • 157 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 98 commits in srepho/Allyanonimiser
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository srepho/Allyanonimiser appears legitimate

⚠ 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 allyanonimiser 
Create a Python-based mini-application named 'PrivacyGuard' that leverages the 'allyanonimiser' package to protect sensitive information in the context of the Australian insurance industry. This tool will read in a CSV file containing customer data and anonymize Personal Identifiable Information (PII) such as names, addresses, phone numbers, and email addresses. Additionally, the application should include features to detect and anonymize medical record identifiers, policy numbers, and any other unique identifiers specific to the insurance sector.

Step 1: Set up the project environment by installing the necessary packages including 'allyanonimiser', pandas, and csv.
Step 2: Develop a function to load the CSV file into a DataFrame using pandas.
Step 3: Implement a series of functions to anonymize different types of PII data based on the capabilities provided by 'allyanonimiser'. Each function should handle a specific type of identifier (e.g., one for names, another for addresses).
Step 4: Create a user-friendly interface where users can select the CSV file to process and choose which types of data they want to anonymize.
Step 5: Integrate error handling and validation checks to ensure the input file is correctly formatted and contains the expected data fields.
Step 6: After processing, allow the user to save the anonymized data back into a new CSV file or view it directly within the application.
Step 7: Finally, document the usage of each function and provide examples of how to use 'allyanonimiser' effectively within PrivacyGuard.

The goal is to create a robust, easy-to-use tool that demonstrates the practical application of 'allyanonimiser' for protecting sensitive data in real-world scenarios.

💬 Discussion Feed

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