archivist-ai

v0.1.0 suspicious
4.0
Medium Risk

Local-first AI image search & management — no cloud, no API keys, 100% private

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low direct execution and network risks, but concerns over metadata and maintenance activity elevate its risk level to suspicious.

  • Non-HTTPS link present
  • Low activity on the git repository
  • Sparse maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Metadata: The presence of a non-HTTPS link, low activity on the git repository, and sparse maintainer history suggest potential risks, but no clear evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

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

Some documentation present

  • Detailed PyPI description (9439 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

  • 96 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 7 commits in abdullahkousa2/archivist-ai
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • (self.model_id) model.eval() if self._quantize and self.device == "cpu":
  • return _VisionEncoder(model).eval() def _make_text_wrapper(model, is_siglip: bool): """T
  • return _TextEncoder(model).eval() # ── Core export function ──────────────────────────────
  • del.from_pretrained(model_id).eval() else: from transformers import CLIPModel, CLIP
  • del.from_pretrained(model_id).eval() # ── Vision encoder ─────────────────────────────────
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:7860
⚠ 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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 archivist-ai 
Create a local-first image management and search application using the 'archivist-ai' Python package. This application will allow users to organize their personal image collections without relying on any cloud services or external APIs, ensuring all data remains private and secure on the user's device. Here are the key functionalities you should include:

1. **Image Upload**: Users should be able to upload images directly from their device into the application.
2. **Local Indexing**: Utilize 'archivist-ai' to create a local index of uploaded images for efficient searching.
3. **Search Functionality**: Implement a feature where users can search for images based on visual content rather than just filenames or tags.
4. **Tagging System**: Allow users to manually tag images for easier categorization and retrieval.
5. **Privacy Assurance**: Emphasize that all data processing happens locally, and no data is ever sent to any server or cloud service.
6. **User Interface**: Design a simple yet intuitive graphical user interface (GUI) using a library like PyQt or Tkinter to make the application user-friendly.
7. **Performance Monitoring**: Include basic performance metrics to monitor the efficiency of the image indexing and search processes.

The goal is to demonstrate how 'archivist-ai' can be leveraged to build robust, privacy-focused applications that handle sensitive image data securely. This project will serve as a practical example of how developers can implement local-first solutions in various domains.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!