autofit

v2026.5.29.4 suspicious
4.0
Medium Risk

Classy Probabilistic Programming

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to its use of pickle.loads for obfuscation, which can pose security risks if misused. However, there are no direct indications of malicious activities such as network calls or shell executions.

  • obfuscation risk due to use of pickle.loads
  • incomplete author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating the package does not execute system commands.
  • Obfuscation: The use of pickle.loads suggests an attempt to obfuscate data, which could be suspicious but might also serve legitimate purposes like data serialization.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The author's information is incomplete and they have only one published package, which could indicate a less experienced or potentially suspicious maintainer.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6920 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

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

Active multi-contributor project

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

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • rn self.string return pickle.loads(self.string) @value.setter def value(self, value):
✓ 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: rghsoftware.co.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository PyAutoLabs/PyAutoFit 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 autofit 
Your task is to develop a probabilistic data analysis tool using the 'autofit' Python package. This tool will be designed to help users understand complex datasets by fitting them to various statistical models automatically. The application should allow users to upload their dataset, select from a range of predefined statistical models, and receive detailed analysis reports including model fits, parameter estimates, and goodness-of-fit measures. Here are the steps and features your project should include:

1. **Data Input**: Implement a user-friendly interface where users can upload their CSV files. Ensure the application can handle common data types and missing values.
2. **Model Selection**: Provide a dropdown menu for selecting different statistical models such as Gaussian Mixture Models, Poisson Regression, etc., which are supported by the 'autofit' package.
3. **Parameter Estimation**: Utilize 'autofit' to automatically estimate parameters for the selected model based on the uploaded dataset.
4. **Goodness-of-Fit Analysis**: Use 'autofit' to perform goodness-of-fit tests and generate visualizations like QQ plots, histograms, and scatter plots comparing observed vs predicted values.
5. **Report Generation**: Create a comprehensive report summarizing the analysis results, including model fit statistics, estimated parameters, and visualizations.
6. **User Interface**: Design a clean, intuitive web-based UI using Flask or Streamlit to interact with the backend processing done via 'autofit'.

Ensure your application is well-documented, with clear instructions for installation and usage. Additionally, provide examples of how to use the application with sample datasets.

💬 Discussion Feed

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