AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risks in terms of network, shell, and obfuscation activities, but its metadata raises concerns due to the repository's recent creation and low activity.
- Low activity and recent creation of the repository
- Single contributor and new maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository's recent creation, low activity, single contributor, and new maintainer increase suspicion of potential malintent.
Package Quality Overall: Low (4.6/10)
✦ High
Test Suite
9.0
Test suite present — 1 test file(s) found
Test runner config found: pyproject.toml1 test file(s) detected (e.g. test_auditweave.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (6029 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
5.0
Partial type annotation coverage
30 type-annotated function signatures detected in source
○ Low
Multiple Contributors
2.0
Single-author or unverifiable project
1 unique contributor(s) across 2 commits in vimalnakrani08/auditweaveSingle author with few commits — possibly a personal or throwaway project
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 10.0
Git history flags: Repository created very recently: 4 day(s) ago (2026-06-03T16:59:22Z)
Repository created very recently: 4 day(s) ago (2026-06-03T16:59:22Z)Repository has zero stars and zero forksVery few commits: 2 totalSingle contributor with only 2 commit(s) — possibly throwaway account
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Vimal Nakrani" 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 auditweave
Create a mini-application named 'DataAuditTrail' that leverages the 'auditweave' package to provide tamper-evident, auditor-navigable logs for a simple data transformation pipeline. This application will take raw CSV data as input, perform basic transformations such as filtering and aggregation, and then output the transformed data while maintaining an immutable record of all actions performed on the data. Step 1: Set up the environment - Install Python and necessary packages including 'pandas', 'auditweave', and any other dependencies required for handling CSV files and logging. Step 2: Design the Data Transformation Pipeline - Define functions to read CSV data into pandas DataFrames. - Implement transformations such as filtering out rows based on certain conditions and aggregating data based on specific columns. - Ensure each transformation step is wrapped in an 'auditweave' context manager to log actions and maintain a chain of custody for the data. Step 3: Create an Audit Trail System - Utilize 'auditweave' to create a tamper-proof log of every action taken on the data during its transformation process. Each entry in the log should include the timestamp, the type of operation performed, the parameters used, and the state of the data before and after the operation. - Implement a feature to export the audit trail as a structured file (e.g., JSON) for easy review by auditors. Step 4: User Interface - Develop a simple command-line interface (CLI) that allows users to specify the input CSV file path, choose from a set of predefined transformations, and view or save the audit trail. - Optionally, implement a basic web UI using Flask or a similar framework to make the tool more accessible. Suggested Features: - Support for adding custom transformation functions to the pipeline. - An option to visualize the audit trail in a graphical format (e.g., using Matplotlib). - Integration with cloud storage services for secure backup and retrieval of audit logs. - A feature to automatically send audit reports via email to designated recipients. By following these steps and incorporating the 'auditweave' package effectively, you'll create a robust tool that not only transforms data but also ensures transparency and accountability throughout the process.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue