asf

v0.1.6 suspicious
4.0
Medium Risk

Algorithm selection framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network and shell execution but has partial obfuscation and incomplete metadata, raising concerns about its authenticity and potential for supply-chain attacks.

  • Partial code obfuscation
  • Incomplete author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: The obfuscation patterns appear to be partial code snippets related to model evaluation and tensor conversion, which could be legitimate but unusual formatting raises some suspicion.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The author information is incomplete and the account seems new or inactive, raising some concerns.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "documentation" -> https://hadarshavit.github.io/asf/latest/
  • Detailed PyPI description (4471 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

  • 316 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in hadarshavit/asf
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • DataFrame: self.model.eval() features_tensor = torch.from_numpy(X.values).to(s
  • t fitted") self.model.eval() features_tensor = torch.from_numpy(X.values).to(s
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: arq

  • "asf" is 2 edit(s) from "arq"
✓ Registered Email Domain

Email domain looks legitimate: aim.rwth-aachen.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hadarshavit/asf appears legitimate

⚠ 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 asf 
Create a Python-based mini-application named 'AlgorithmSelector' that leverages the 'asf' package to help users select the most appropriate algorithm for their specific data processing tasks. This application will serve as a user-friendly interface where users can input details about their datasets and the type of task they wish to perform (e.g., classification, regression, clustering). The app will then use 'asf' to recommend suitable algorithms based on the provided information.

The application should have the following core functionalities:
1. User Interface: Develop a simple and intuitive command-line interface (CLI) for interacting with the application. Users should be able to easily input details such as dataset characteristics (size, dimensionality), the task at hand (classification, regression, clustering), and any other relevant parameters.
2. Algorithm Recommendation: Utilize the 'asf' package to analyze the inputted dataset and task details. The application should then recommend one or more algorithms that best fit the user's requirements, explaining why these particular algorithms were selected.
3. Performance Metrics: For each recommended algorithm, provide an overview of its expected performance metrics based on common benchmarks and theoretical considerations. This includes accuracy, precision, recall, F1 score, etc., depending on the type of task.
4. Integration Capabilities: Allow users to export the recommendation details into a structured format (JSON or CSV) for further analysis or direct integration into other projects.
5. Documentation: Provide comprehensive documentation detailing how to install and run the application, along with examples of typical usage scenarios.

To utilize the 'asf' package effectively, ensure you explore its core functionalities such as algorithm evaluation, comparison, and selection criteria. Incorporate these aspects into your application design to make it robust and versatile, catering to a wide range of data science and machine learning needs.

💬 Discussion Feed

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