apricity

v0.6.0 suspicious
6.0
Medium Risk

Apricity — a local, zero-dependency front-end for the MemPalace personal-memory system. Browse, search, edit, and curate your memories and knowledge graph from your browser.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its execution of shell commands and making network calls, which could indicate potential malicious activity. However, without concrete evidence of harmful intent, it remains suspicious.

  • High shell risk due to potential for arbitrary code execution
  • Moderate network risk due to external URL interactions
Per-check LLM notes
  • Network: The package makes network calls to an external URL, which could be used for legitimate purposes like fetching updates but also raises concerns about potential unauthorized data transmission.
  • Shell: Executing shell commands can be necessary for certain functionalities but poses significant risks as it allows the package to run arbitrary code on the user's system, potentially leading to security breaches.
  • Obfuscation: The presence of base64 decoding suggests potential obfuscation, but it could also be part of legitimate cryptographic operations.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: The presence of non-secure links and the novelty of both the package and maintainer warrant caution, but insufficient evidence for high suspicion.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (23942 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 :: Typed
  • 77 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 51 commits in epinethrone/apricity
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • sion"] try: req = urllib.request.Request( _GITHUB_LATEST_URL, headers
  • }, ) with urllib.request.urlopen(req, timeout=3) as resp: data = json.loa
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • terations_str) salt = base64.b64decode(salt_b64) expected = base64.b64decode(hash_b64)
  • (salt_b64) expected = base64.b64decode(hash_b64) except (ValueError, TypeError): return
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • turn cached[2] proc = subprocess.run( cmd, input=raw, capture_output=True, timeout=30
  • dumps(result)) """ proc = subprocess.run( [str(MEMPALACE_PYTHON), "-c", code], input=
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8765
  • Non-HTTPS external link: http://127.0.0.1:8765/api/memories
✓ Git Repository History

Repository epinethrone/apricity appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Apricity contributors" 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 apricity 
Create a fully-functional mini-application using the 'apricity' package that serves as a user-friendly interface for managing a personal memory system. This application should allow users to create, view, edit, and delete their memories and knowledge graphs directly through a web browser interface. Here are the key steps and features to include:

1. **Setup**: Begin by installing the 'apricity' package if it isn't already installed in your Python environment. Ensure you have a basic understanding of how the package works.
2. **Application Structure**: Design a simple yet effective structure for your application. It should consist of HTML/CSS for the frontend and Python (using Flask or Django) for the backend.
3. **User Interface**: Develop a clean and intuitive UI that allows users to easily navigate between viewing all memories, searching for specific memories, and editing existing ones.
4. **Memory Management**: Implement functionality to add new memories, which includes specifying details such as title, description, tags, and associated dates. Memories should also be editable and deletable from within the app.
5. **Search Functionality**: Integrate a robust search feature that enables users to find memories based on keywords, tags, or dates.
6. **Integration with Apricity**: Utilize 'apricity' to store and manage the data behind the scenes. Ensure that all interactions with the database are handled through 'apricity', making sure to leverage its zero-dependency nature.
7. **Testing**: Thoroughly test the application to ensure all features work as expected. Pay special attention to edge cases like handling large numbers of memories or complex queries.
8. **Deployment**: Once tested, deploy the application to a hosting service of your choice, ensuring it's accessible online.

By following these steps and implementing these features, you'll create a powerful tool for anyone looking to manage and expand their personal knowledge base efficiently.

💬 Discussion Feed

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