🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk due to non-HTTPS links and low maintainer activity, although it does not clearly indicate malicious intent. Further investigation is required.

Suspicious non-HTTPS links
Low maintainer activity

Per-check LLM notes

Network: Network calls are likely for API-based anonymization services, which align with the package's presumed functionality.
Shell: Shell executions may be related to running additional scripts or services that support the anonymization process, but require further investigation to ensure they are not performing unauthorized actions.
Metadata: The package has suspicious non-HTTPS links and low maintainer activity, but no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
24 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (36797 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

264 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 76 commits in sedoglia/AI-Privacy-Anonymizer
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

import requests return requests.get(f"{_BASE_URL}{path}", timeout=10, **kw) def _post_text(tex
import requests return requests.post( f"{_BASE_URL}/anonymize/text", data={"text"
import requests return requests.post( f"{_BASE_URL}/anonymize/file", files={"file
mport requests resp = requests.post(f"{_BASE_URL}/anonymize/text", data={"mode": "replace"}, tim
mport requests resp = requests.post(f"{_BASE_URL}/anonymize/file", data={"mode": "replace"}, tim

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

s_cmd)}") extras_result = subprocess.run(extras_cmd, check=False) if extras_result.returncode !=
(opf_cmd)}") opf_result = subprocess.run(opf_cmd, check=False) if opf_result.returncode != 0:
requests) + "\n" result = subprocess.run( [sys.executable, "-m", "privacy_anonymizer.mcp_serv
+ "\n\n" ) result = subprocess.run( [sys.executable, "-m", "privacy_anonymizer.mcp_serv

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 8.0

Found 4 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8000/anonymize/text
Non-HTTPS external link: http://127.0.0.1:8000/anonymize/file
Non-HTTPS external link: http://127.0.0.1:8000/docs`.
Non-HTTPS external link: http://`,

⚠ 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 2.0

1 maintainer concern(s) found

Author "Sergio Dogliani" 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 ai-privacy-anonymizer

Your task is to develop a mini-application named 'PrivacyGuard' that leverages the 'ai-privacy-anonymizer' Python package to ensure local-first privacy for Italian personal data, adhering strictly to GDPR regulations. This application will serve as a tool for individuals and organizations to anonymize sensitive information within documents before sharing them digitally.

Step 1: Setup
- Begin by setting up your Python environment. Ensure you have Python installed along with the 'ai-privacy-anonymizer' package.
- Create a virtual environment for your project and install the required dependencies.

Step 2: Application Structure
- Design a simple user interface where users can upload text files or copy-paste text into a text area.
- Implement functionality to detect and anonymize PII (Personal Identifiable Information) such as names, addresses, emails, phone numbers, and other sensitive data types specific to Italy.
- Use the 'ai-privacy-anonymizer' package to process the input text and replace PII with generic placeholders.

Step 3: Core Functionality
- Develop a function that reads the uploaded/copy-pasted text and processes it using the 'ai-privacy-anonymizer'.
- Ensure that the anonymization process is reversible if needed (e.g., by storing a mapping of original PII to anonymized data).
- Implement a feature to preview the anonymized text before finalizing the operation.

Step 4: Additional Features
- Add a feature to export the anonymized text as a new file or copy it to the clipboard.
- Include options for different anonymization styles (e.g., replacing names with titles like 'Mr./Ms.', masking email addresses with placeholders).
- Integrate a tutorial or help section explaining the importance of data privacy and how to use PrivacyGuard effectively.

Step 5: Testing & Deployment
- Thoroughly test the application with various types of Italian PII data to ensure accurate anonymization.
- Consider deploying the application on a platform like GitHub Pages or Heroku for public access.

By following these steps, you'll create a useful and practical tool for safeguarding personal data in compliance with GDPR standards.