🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning behaviors such as network calls and code obfuscation, though it lacks clear indicators of malicious activity. Given the new maintainer's account and single package history, there is a moderate risk that warrants further investigation.

network calls to external resources
code obfuscation practices
new maintainer with limited history

Per-check LLM notes

Network: The package makes network calls to fetch content from a webpage, which is common but should be reviewed for legitimacy and security.
Shell: No shell execution patterns were detected, indicating low risk.
Obfuscation: The observed patterns suggest code obfuscation but do not clearly indicate malicious intent; they could be part of normal model evaluation processes.
Credentials: No clear signs of credential harvesting were detected in the provided snippets.
Metadata: The maintainer has a new account and only one package, which could indicate potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

página web.""" response = requests.get(url) response.raise_for_status() return BeautifulSou

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

kenizer_fn self.model.eval() # Poner el modelo en modo de evaluación def invoke(se
) self.model.eval() total_loss = 0.0 num_batches = le
with self.target_network.eval(): next_q_values = self.target_network(next_stat
""" self.model.eval() self.kv_cache.clear() batch_size
return self def eval(self): return self.train(False) def state_dict(

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yoqer/TenMiNaTor appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "yoqer" 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 Tenminator2

Create a mini-application that utilizes the 'Tenminator2' deep learning framework to classify handwritten digits from the MNIST dataset. This application will serve as a simple yet effective tool to demonstrate the capabilities of 'Tenminator2' in handling image classification tasks. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Ensure you have Python installed along with the necessary libraries such as 'Tenminator2', 'numpy', and 'matplotlib'. Use pip to install these packages if they are not already installed.

2. **Data Preparation**: Download the MNIST dataset using a library like TensorFlow or PyTorch. Preprocess the data by normalizing pixel values and splitting it into training and testing sets.

3. **Model Building**: Using 'Tenminator2', create a neural network model suitable for image classification. The model should include layers such as Convolutional Layers, Pooling Layers, and Dense Layers. Experiment with different architectures to find the best one for your application.

4. **Training the Model**: Train the model using the training set. Monitor the accuracy and loss over epochs to ensure the model is learning effectively. Implement early stopping to prevent overfitting.

5. **Evaluation**: Evaluate the trained model using the test set to measure its performance. Calculate metrics such as accuracy, precision, recall, and F1 score.

6. **Prediction Interface**: Develop a simple user interface where users can input their own handwritten digit images (e.g., via a file upload) and get predictions from the model. This could be a basic command-line interface or a web-based frontend.

7. **Documentation and Reporting**: Document the entire process, including the model architecture, training details, and evaluation results. Create a report summarizing the findings and potential improvements for future work.

Suggested Features:
- Include visualizations of the training process (e.g., accuracy vs. epoch plots).
- Allow users to adjust hyperparameters of the model through the interface.
- Implement real-time prediction feedback in the user interface.

This project will not only showcase the power of 'Tenminator2' but also provide a practical tool for understanding and experimenting with deep learning models.