AI Analysis
Final verdict: SUSPICIOUS
The package shows high obfuscation risk and metadata concerns, raising suspicions about its legitimacy despite no clear evidence of malicious activity.
- High obfuscation risk
- Lack of maintainer information
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution detected, reducing risk of direct system command misuse.
- Obfuscation: The observed patterns suggest deliberate obfuscation which may hide malicious activities or complex logic, warranting further investigation.
- Credentials: No clear signs of credential harvesting detected, but the presence of obfuscation raises suspicion.
- Metadata: The package is suspicious due to its newness and lack of maintainer information, which could indicate potential malice.
Package Quality Overall: Medium (6.6/10)
✦ High
Test Suite
9.0
Test suite present — 13 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml13 test file(s) detected (e.g. conftest.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/pritesh-2711/anonypii/blob/main/README.mdDetailed PyPI description (6190 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 :: Typed150 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 17 commits in pritesh-2711/anonypiiTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
del.to(dev) model.eval() return tok, model, dev except (ModelLo
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 pritesh-2711/anonypii appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" 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 anonypii
Create a privacy-focused mini-application called 'DataGuard' using the 'anonypii' Python package. DataGuard will serve as a versatile tool for developers and data analysts to protect sensitive information within datasets before sharing them for analysis or storage. The application should have a user-friendly interface where users can upload a CSV file containing personal data. Once uploaded, DataGuard will automatically detect and anonymize any personally identifiable information (PII) such as names, addresses, phone numbers, emails, and social security numbers. Additionally, it should provide options to either mask the data irreversibly or anonymize it reversibly if required by specific use cases. After processing, DataGuard should allow users to download the sanitized dataset. Furthermore, implement a feature that allows users to specify custom patterns or regular expressions for detecting and anonymizing additional types of sensitive data not covered by default. Ensure that the application logs all anonymization actions performed for audit purposes. Use 'anonypii' throughout the process to handle the detection and anonymization of PII data, leveraging its production-grade capabilities and fine-tuned DeBERTa models for accurate and reliable results.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue