CSET

v26.5.0 suspicious
4.0
Medium Risk

Toolkit for evaluation and investigation of numerical models for weather and climate applications.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its use of shell commands and network calls, which require further scrutiny to ensure they are used for legitimate purposes.

  • High shell risk
  • Moderate network risk
Per-check LLM notes
  • Network: The use of urllib to make network calls could be legitimate but requires further investigation into the purpose of these calls.
  • Shell: Executing shell commands can pose significant risks, especially when not clearly documented. This may indicate unexpected behavior or potential misuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: with urllib.request.urlopen(file_path, timeout=30, context=ctx) as response:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • n(moo_command)}") p = subprocess.run(moo_command) if p.returncode > 0: loggin
  • if recipe_count: subprocess.run(message_command + ["start baking"], check=True) else
  • ue) else: subprocess.run(message_command + ["skip baking"], check=True) if __name__
  • ithub.com/MetOffice/CSET" subprocess.run( f'printf "{body}" | mail -s "{subject}" "$USER"',
  • 9 for an example. subprocess.run( ("xdg-open", str(save_path)), check=True, s
  • , check=True, shell=True, ) if __name__ == "__main__": # pragma: no cover
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository MetOffice/CSET appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Met Office, NIWA" 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 CSET 
Develop a mini-application named 'ClimateModelEvaluator' using the Python package 'CSET', which is designed for evaluating and investigating numerical models for weather and climate applications. This application aims to provide researchers and climate scientists with a user-friendly tool to analyze and compare different climate models based on their performance metrics.

### Core Features:
1. **Model Input**: Allow users to upload or specify multiple climate models they wish to evaluate. Each model will have its unique set of parameters and simulation data.
2. **Evaluation Metrics**: Implement a suite of evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and others relevant to climate modeling. These metrics will help assess the accuracy and reliability of the models.
3. **Visualization Tools**: Provide graphical representations of the model outputs and their corresponding evaluation metrics. Visuals could include time-series plots, geographical maps, and scatter plots comparing observed vs. simulated data.
4. **Report Generation**: Automatically generate comprehensive reports summarizing the evaluation results. Reports should include tables of key statistics, visualizations, and a brief analysis of each model's strengths and weaknesses.
5. **Interactive Dashboard**: Create an interactive dashboard where users can select different models, view their performance metrics, and customize the visualizations according to their needs.

### Utilization of CSET Package:
- **Data Handling**: Use CSET to handle and preprocess the input datasets from various climate models. This includes reading in model outputs, managing large datasets efficiently, and preparing them for analysis.
- **Model Evaluation**: Leverage CSET’s built-in functions for calculating evaluation metrics. Users should be able to choose from a variety of metrics supported by CSET, depending on the specific requirements of their study.
- **Custom Analysis**: Enable advanced users to perform custom analyses using CSET’s toolkit. This could involve running sensitivity tests, exploring different scenarios, or applying machine learning techniques to predict future climate trends based on historical data.
- **Integration with Other Tools**: Ensure seamless integration with other scientific tools and libraries commonly used in climate research, enhancing the applicability and flexibility of ClimateModelEvaluator.

This project will serve as a powerful tool for climate scientists, providing them with an intuitive interface and robust analytical capabilities to better understand and predict climate patterns.