archive-r-python

v0.1.32 suspicious
5.0
Medium Risk

Python bindings for archive_r: libarchive-based streaming traversal for recursive nested archives (no temp files, no large in-memory buffers)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to its shell execution capability and low repository activity, raising concerns about its reliability and intentions.

  • Shell risk detected
  • Low repository activity
Per-check LLM notes
  • Network: No network calls detected, which is normal and not suspicious.
  • Shell: Detection of shell execution may indicate the package performs system-level tasks, but without context, it's hard to determine if it's intended functionality or malicious. Further investigation is needed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The low activity in the repository and the maintainer's history suggest potential unreliability, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_traverser.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Raizo-TCS/archive_r#readme
  • Detailed PyPI description (15959 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

  • 18 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in Raizo-TCS/archive_r
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • bootstrap_cmd}") result = os.system(bootstrap_cmd) if result != 0: raise RuntimeErro
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ 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 "archive_r Team" 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 archive-r-python 
Create a Python-based mini-application named 'Archiver' that leverages the 'archive-r-python' package to manage nested archive files without using temporary files or large in-memory buffers. This application will serve as a versatile tool for extracting and managing complex archive structures efficiently.

**Application Features:**
1. **Recursive Extraction:** Users should be able to provide a path to a nested archive file (e.g., .zip within a .tar.gz), and the application will recursively extract all contents into a specified output directory.
2. **Streaming Support:** Implement streaming support to ensure that only necessary parts of the archive are loaded into memory at any given time, making it efficient for handling very large files.
3. **Compression Options:** Allow users to specify different compression formats for the final extracted files, such as .zip, .tar.gz, or .rar, while ensuring that the original structure of the nested archives is preserved.
4. **Error Handling:** Robust error handling to gracefully manage issues like corrupted archives, unsupported formats, or permission errors.
5. **User Interface:** Develop a simple command-line interface (CLI) that guides users through the process of specifying input and output paths, choosing compression options, and viewing real-time extraction progress.
6. **Logging and Reporting:** Integrate logging to record the extraction process, including timestamps, file paths, and any encountered errors. Provide a summary report upon completion detailing the number of successfully extracted files, skipped files, and any errors.

**How 'archive-r-python' is Utilized:**
- Use 'archive-r-python' for the core functionality of traversing and extracting nested archives. This includes reading headers, extracting files, and handling various archive types seamlessly.
- Leverage the streaming capabilities of 'archive-r-python' to avoid loading entire archives into memory, which is crucial for managing large datasets efficiently.
- Ensure that the application supports a wide range of archive formats supported by 'archive-r-python', thereby enhancing its versatility and usability.

💬 Discussion Feed

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