arxanon

v1.0.0 suspicious
5.0
Medium Risk

Cross-domain structural analogy discovery engine for AI/ML researchers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to unexpected network calls and incomplete metadata, raising concerns about its origin and purpose.

  • Unexpected network calls to external APIs
  • Incomplete maintainer history and sparse repository activity
Per-check LLM notes
  • Network: The package makes unexpected network calls to external APIs which may indicate unauthorized data transmission.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
  • Metadata: The package shows several red flags including lack of maintainer history, sparse git repository activity, and an incomplete author profile.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Design Document" -> https://github.com/Serhii2009/arxanon/blob/main/ARXANON_DESI
  • Detailed PyPI description (10555 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 8 commits in Serhii2009/arxanon
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • }).encode() req = urllib.request.Request( "https://openrouter.ai/api/v1/chat/com
  • try: with urllib.request.urlopen(req, timeout=timeout) as r: dat
  • p try: resp = requests.get(f"{base_url}/api/tags", timeout=3) resp.raise_for_s
  • port requests resp = requests.get(f"{config.OLLAMA_BASE_URL}/api/tags", timeout=3) re
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.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 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 arxanon 
Create a mini-application named 'Analogizer' that leverages the 'arxanon' package to discover cross-domain structural analogies between different datasets. This tool will be particularly useful for AI/ML researchers who want to identify similarities between seemingly unrelated datasets to gain new insights and potentially apply successful methodologies from one domain to another.

**Application Overview:**
- **User Interface:** A simple web-based interface using Flask (a lightweight WSGI web application framework written in Python).
- **Core Functionality:** Users will upload two datasets (CSV files), and the application will use 'arxanon' to analyze these datasets and find structural analogies between them.
- **Output:** Results will be presented in a user-friendly manner, highlighting key similarities and differences between the datasets.

**Features:**
1. **Dataset Upload:** Allow users to upload CSV files containing their datasets.
2. **Data Preprocessing:** Implement basic data cleaning and preprocessing steps such as handling missing values, normalizing data, etc., before feeding it into 'arxanon'.
3. **Analogy Discovery:** Utilize 'arxanon' to perform cross-domain structural analogy discovery on the uploaded datasets.
4. **Visualization:** Provide visual representations of the discovered analogies through charts or graphs, helping users understand the similarities and differences more intuitively.
5. **Report Generation:** Generate a detailed report summarizing the findings, including statistical measures and visual aids.
6. **User Authentication:** Optional feature where users can create accounts and save their analysis results for future reference.

**Steps to Build the Application:**
1. Set up a Flask environment and install necessary packages including 'arxanon', pandas, matplotlib, and any other dependencies.
2. Develop the dataset upload functionality ensuring proper validation of file types.
3. Implement the data preprocessing logic to prepare the datasets for 'arxanon'.
4. Integrate 'arxanon' into your application to enable analogy discovery capabilities.
5. Design and implement visualization components to display the results effectively.
6. Create a report generation module to compile detailed summaries of the analysis.
7. Optionally, add user authentication features using Flask-Security or similar libraries.
8. Test the application thoroughly to ensure all functionalities work as expected.
9. Deploy the application to a cloud platform like Heroku or AWS for wider accessibility.

💬 Discussion Feed

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