arraylake

v1.0.3 suspicious
6.0
Medium Risk

Python client for ArrayLake

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits potential obfuscation techniques through the use of pickle without a clear purpose, raising concerns about hidden malicious activities.

  • use of pickle for object serialization and deserialization without clear purpose
  • potential obfuscation to evade simple code analysis
Per-check LLM notes
  • Obfuscation: The use of pickle for object serialization and deserialization without clear purpose may indicate obfuscation to evade simple code analysis.
  • Credentials: No direct evidence of credential harvesting is present, but the suspicious usage of pickle could be part of a larger pattern aimed at hiding malicious activities.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.earthmover.io/
  • Brief PyPI description (648 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

  • 325 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ) client = await httpx.AsyncClient( base_url=api_url, transport=transpo
  • ) -> dict[str, str]: with httpx.Client() as client: response = client.get(service_uri)
  • y(self.proxy) return httpx.Client(**client_kwargs) def _create_async_client(self, **kwarg
  • y(self.proxy) return httpx.AsyncClient(**client_kwargs) @cached_property def auth_provider
  • tpx.codes.OK)) async with httpx.AsyncClient(auth=auth) as client: await client.get("https://foo.
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • quest.headers) client2 = pickle.loads(pickle.dumps(client)) await check_client(client2) a
  • assert sid restored = pickle.loads(pickle.dumps(client)) assert restored._default_headers["
✓ 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: earthmover.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 arraylake 
Create a mini-application that leverages the ArrayLake Python client to manage and analyze large-scale numerical datasets efficiently. This application will serve as a data analysis tool for researchers and data scientists who need to handle extensive arrays of numbers without compromising on performance.

### Application Overview:
- **Name**: DataAnalyzer
- **Purpose**: To provide an intuitive interface for importing, managing, and analyzing large numerical datasets using ArrayLake.
- **Features**:
  - Import datasets from various sources (CSV, Excel, etc.)
  - Store datasets in ArrayLake for efficient access and manipulation
  - Perform basic statistical analyses (mean, median, mode, standard deviation)
  - Visualize data through plots (line graphs, histograms)
  - Export analyzed data back to file formats like CSV or Excel

### Step-by-Step Implementation:
1. **Setup Environment**:
   - Install necessary packages including `arraylake` and other dependencies such as pandas and matplotlib for data handling and visualization.
2. **Data Importation**:
   - Implement functions to import data from different file types into Python structures.
3. **ArrayLake Integration**:
   - Use `arraylake` to store imported data in an efficient manner, ensuring fast access and manipulation capabilities.
4. **Data Analysis**:
   - Develop functions within the application to perform statistical analyses on the stored data.
5. **Visualization**:
   - Utilize matplotlib to create visual representations of the data and analysis results.
6. **Export Results**:
   - Provide functionality to export the analyzed data and visualizations back into user-specified file formats.

### Utilizing `arraylake` Package:
- **Storage**: Use ArrayLake to store the numerical datasets, leveraging its optimized storage mechanisms for large-scale data.
- **Performance**: Ensure all operations (import, analysis, visualization) are optimized by utilizing ArrayLake's performance benefits.
- **Integration**: Integrate ArrayLake seamlessly into the application's workflow to ensure smooth data management and processing.

This project aims to showcase the power and efficiency of ArrayLake in handling large datasets, providing a practical example for real-world applications.

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