attnhut

v0.4.1 suspicious
5.0
Medium Risk

Implementation of various Transformer Attention mechanisms proposed by frontier LLM labs.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, and credential handling. However, the recent creation of the repository and the lack of maintainer details raise concerns about potential supply-chain risks.

  • Recent repository creation
  • Missing maintainer details
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 direct system command execution.
  • Obfuscation: The observed patterns appear to be related to model evaluation and tensor operations, likely part of normal functionality rather than obfuscation.
  • Credentials: No evidence of credential harvesting or secret handling was detected.
  • Metadata: The repository was created very recently and the maintainer has a new or inactive account with missing author information, indicating potential risks.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

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

Some documentation present

  • Detailed PyPI description (11170 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 5 commits in egmaminta/attnhut
  • 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 6.0

Found 3 obfuscation pattern(s)

  • earlier output.""" module.eval() x = torch.randn(batch, t, dim) with torch.no_grad(
  • m = _model(depth=2) m.eval() slots = torch.randn(2, 3, 5, 32) perm = torch.tens
  • , window=4, sink=0, stride=1).eval() x = torch.randn(1, 12, 32) out = m(x) q, k, v
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: up.edu.ph>

  • Very short email domain: up.edu.ph>
✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-03T23:52:35Z)

  • Repository created very recently: 4 day(s) ago (2026-06-03T23:52:35Z)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 attnhut 
Create a mini-application named 'AttentionExplorer' that allows users to experiment with different types of Transformer Attention mechanisms implemented in the 'attnhut' package. This application should serve as both an educational tool and a practical playground for developers interested in understanding and experimenting with attention mechanisms. Here are the steps and features to include:

1. **User Interface**: Design a simple and intuitive web-based UI using Flask, a lightweight Python web framework. The UI should allow users to select from a variety of attention mechanisms available in 'attnhut', such as Multi-Head Attention, Relational Attention, etc.
2. **Configuration Settings**: Users should be able to configure parameters such as input sequence length, number of heads, key/query/value dimensions, and any other relevant settings for the selected attention mechanism.
3. **Visualization Tools**: Implement real-time visualization of the attention weights using libraries like Matplotlib or Plotly. These visualizations should help users understand how different configurations affect the attention distribution.
4. **Interactive Examples**: Provide pre-configured examples for each type of attention mechanism, allowing users to quickly see the effects of changing parameters without having to manually set everything up.
5. **Documentation and Help**: Include comprehensive documentation within the application explaining each feature and how it relates to the underlying attention mechanisms. Also, provide tooltips and help sections directly within the UI.
6. **Performance Metrics**: Display performance metrics such as computation time and memory usage for each operation, helping users understand the efficiency of different attention mechanisms under various conditions.
7. **Saving and Sharing Results**: Allow users to save their configurations and results, and share them via a unique URL or download option.

Utilize the 'attnhut' package extensively throughout the application, particularly in the backend where attention mechanisms are calculated and visualized. Ensure that the integration is seamless and that the application demonstrates the full capabilities of 'attnhut'.

💬 Discussion Feed

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