AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to potential obfuscation and low repository activity. Further investigation is recommended.
- Obfuscation risk detected
- Low repository activity
Per-check LLM notes
- Network: No network calls detected, which is typical unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no direct system command invocations.
- Obfuscation: The code snippet shows patterns that could be obfuscated, but without context, it's hard to determine if it's malicious or just part of a machine learning evaluation process.
- Credentials: No obvious signs of credential harvesting were detected.
- Metadata: The repository's low activity and lack of community engagement suggest potential risk.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 10.0
Found 6 obfuscation pattern(s)
roc_auc_score model.eval() with torch.no_grad(): logits = model(Xt.json") self.model_.eval() with torch.no_grad(): logits = self.moarray] = [] model.eval() with torch.no_grad(): for xb,hreshold self.model_.eval() with torch.no_grad(): val_logits = sel._device_ self.model_.eval() # Batch inference so large test folds don't OOM onate_dict(state) model.eval() instance.model_ = model instance._device_
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: unibo.it>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 5.0
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksSingle contributor with only 4 commit(s) — possibly throwaway account
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 MaldiDeepKit
Create a mini-application that leverages the 'MaldiDeepKit' Python package to classify MALDI-TOF binned spectra. Your application should allow users to upload their own binned spectrum data and then use 'MaldiDeepKit' to predict the class of the uploaded spectrum. Here’s a detailed breakdown of the steps and features your application should include: 1. **User Interface Design**: Develop a clean, user-friendly interface where users can easily upload their spectrum data files (CSV format). 2. **Data Preprocessing**: Implement functionality within your application to preprocess the uploaded data according to best practices for MALDI-TOF spectra, such as normalization and binning. 3. **Model Selection**: Integrate 'MaldiDeepKit' to provide a selection of pre-trained deep learning models compatible with sklearn. Users should be able to choose from at least three different models available in 'MaldiDeepKit'. 4. **Prediction**: Once a model is selected, the application should use the chosen model to make predictions on the uploaded data and display the predicted class. 5. **Visualization**: Include visualizations of the input data and the classification results to help users better understand the prediction process. 6. **Performance Metrics**: Display performance metrics of the selected model when applied to the uploaded dataset, such as accuracy, precision, recall, and F1-score. 7. **Documentation**: Provide clear documentation on how to use the application, including explanations of the preprocessing steps, model selection, and interpretation of the results. This project will not only demonstrate the practical application of 'MaldiDeepKit' but also serve as a useful tool for researchers working with MALDI-TOF binned spectra.