amuletml

v0.5.1 safe
3.0
Low Risk

Amulet is a Python machine learning (ML) package to evaluate the susceptibility of different risks to security, privacy, and fairness.

🤖 AI Analysis

Final verdict: SAFE

The package has low risk indicators with no network or shell execution detected, suggesting it does not engage in potentially harmful activities like supply-chain attacks. However, the lack of detailed metadata raises some concern.

  • No network calls detected
  • No shell execution patterns detected
  • Lacks author details and effort indicators
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute external commands.
  • Metadata: Low risk, but lacks author details and effort indicators.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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 (2460 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
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 88 commits in ssg-research/amulet
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • .target_model.to(self.device).eval() def __invert_cost(self, input_x: torch.Tensor) -> tor
  • """ self.model.eval() predictions = [] targets = [] attr
  • """ discriminator.eval() main_model.eval() n_attrs = None p
  • tor.eval() main_model.eval() n_attrs = None preds: list[list[float]] =
  • filename) model.eval() model.requires_grad_(False) models
  • elf.device) model.eval() if self.device.startswith("cuda"):
✓ 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: uwaterloo.ca>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ssg-research/amulet appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amuletml 
Create a mini-application named 'RiskAssessor' using the Python package 'amuletml'. This application will serve as a tool for evaluating the risk levels associated with various datasets concerning security, privacy, and fairness. Here’s a detailed step-by-step guide on how to develop this application:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have Python installed and create a virtual environment for your project. Install necessary packages including 'amuletml', pandas, numpy, and matplotlib for data manipulation and visualization.

2. **Data Collection**: Collect a dataset that includes information about individuals or entities where you want to assess risks related to security, privacy, and fairness. For example, you could use a synthetic dataset containing personal information such as age, gender, income level, and location.

3. **Data Preprocessing**: Clean the dataset by handling missing values, removing duplicates, and normalizing the data if necessary. Use pandas for these operations.

4. **Risk Assessment Model Development**: Utilize 'amuletml' to develop models that assess the risk levels of different attributes within the dataset. For instance, determine how sensitive each attribute is to privacy breaches, security threats, and fairness issues. This involves training models using the provided dataset and interpreting their outputs.

5. **Visualization**: Implement visualizations to represent the risk levels of different attributes. Use matplotlib to create graphs and charts that clearly illustrate which attributes pose higher risks compared to others.

6. **User Interface**: Develop a simple user interface using a framework like Streamlit or Flask where users can upload their own datasets and get a risk assessment report. The UI should display the processed data and risk levels in an easily understandable format.

7. **Testing & Documentation**: Thoroughly test the application to ensure it works as expected. Document all steps involved in setting up the environment, running the application, and interpreting the results. Include examples and explanations of how the 'amuletml' package is utilized throughout the documentation.

By completing these steps, you will have built a fully-functional mini-application that leverages 'amuletml' to help users understand and mitigate potential risks in their datasets.

💬 Discussion Feed

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