aisquare-pipe-local

v0.1.0 suspicious
6.0
Medium Risk

Local filesystem connector for aisquare.pipe

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its high credential risk and low maintenance level. It attempts to read sensitive files, which raises suspicion.

  • High credential risk due to reading sensitive files
  • Low metadata quality suggesting lack of maintenance
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The code attempts to read sensitive files which may indicate an attempt at credential harvesting.
  • Metadata: The package shows low maintenance and metadata quality, which could indicate potential risks but lacks clear malicious indicators.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • Test runner config found: pyproject.toml
  • 4 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1767 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

  • 19 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • client.read_file("../../etc/passwd") def test_traversal_blocked_on_write(self, sample_con
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "A-Square" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aisquare-pipe-local 
Create a file management utility called 'FilePipe' using Python, which leverages the 'aisquare-pipe-local' package to interact with local filesystems. This utility will serve as a bridge between your local files and a more complex data pipeline setup, allowing users to easily manage, organize, and process their files through simple commands. Here’s a detailed plan on how to develop 'FilePipe':

1. **Setup**: Start by installing Python and setting up a virtual environment. Then install 'aisquare-pipe-local' along with other necessary packages such as pandas for data manipulation and matplotlib for visualization.
2. **Core Functionality**: Implement basic functionalities such as listing files within a directory, moving files between directories, renaming files, and deleting files. Use 'aisquare-pipe-local' to connect to the local filesystem and perform these operations efficiently.
3. **Advanced Features**: Extend the utility to include more advanced features like:
   - **Filtering**: Allow users to filter files based on criteria such as file type, date modified, size, etc., using regular expressions and other filtering techniques.
   - **Batch Operations**: Enable batch processing of files, such as renaming all files in a directory according to a specified pattern or moving multiple files at once.
   - **Data Visualization**: Integrate matplotlib to visualize file metadata such as file sizes over time or frequency of file types in a directory.
4. **User Interface**: Develop a simple command-line interface (CLI) for interacting with 'FilePipe'. Consider adding options for both interactive and non-interactive use, such as running scripts with predefined commands.
5. **Documentation & Testing**: Write comprehensive documentation for each feature and ensure thorough testing to validate functionality across different scenarios.
6. **Deployment**: Package 'FilePipe' as a standalone executable or a pip-installable package, making it easy for others to use and contribute to.

The 'aisquare-pipe-local' package plays a crucial role in connecting to the local filesystem, ensuring efficient and reliable interaction with files. It simplifies the handling of file paths and operations, making it easier to focus on developing the utility's unique features.

💬 Discussion Feed

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