AI Analysis
Final verdict: SUSPICIOUS
The package is assessed as suspicious due to potential typosquatting and the maintainer's limited history with PyPI. However, there are no immediate signs of malicious intent such as network risks, shell risks, or credential harvesting.
- Potential typosquatting targeting 'toml'
- Maintainer has limited history with PyPI
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar exploits.
- Obfuscation: The observed patterns appear to be related to the evaluation mode of a neural network model and do not indicate malicious obfuscation.
- Credentials: No credential harvesting patterns were detected.
- Metadata: The package may be a typosquatting attempt and the maintainer has a limited history with PyPI.
- ⚠ Typosquatting target: toml
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 6.0
Found 3 obfuscation pattern(s)
else: self.model.eval() with torch.no_grad(): output =el_cpu.training model_cpu.eval() # Default names if input_names is None: imodel = TinyNet() model.eval() dummy = torch.randn(1, 1, 8, 8) onnx_path = tmp_p
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
score 9.0
Possible typosquat of: toml, tox, moto
"ToTf" is 2 edit(s) from "toml""ToTf" is 2 edit(s) from "tox""ToTf" is 2 edit(s) from "moto"
Registered Email Domain
Email domain looks legitimate: vitstudent.ac.in
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Akshay, Shyamanth Reddy" 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 ToTf
Create a machine learning model comparison tool using the 'ToTf' library that allows users to easily switch between PyTorch and TensorFlow models without changing their codebase. This tool will enable developers to quickly evaluate the performance of different neural network architectures on a given dataset across both frameworks. Here’s how you can structure your project: 1. **Project Setup**: Start by setting up your Python environment. Ensure that both PyTorch and TensorFlow are installed along with the 'ToTf' package. 2. **Model Definition**: Define a set of common neural network architectures (e.g., CNN, RNN, LSTM) using 'ToTf'. These models should be compatible with both PyTorch and TensorFlow seamlessly. 3. **Data Preparation**: Use a popular dataset like CIFAR-10 or MNIST for image classification tasks. Preprocess the data as required for training and testing. 4. **Training Loop**: Implement a training loop that can switch between PyTorch and TensorFlow backends dynamically. This should include options to specify the backend during runtime. 5. **Evaluation Module**: Develop an evaluation module that compares the accuracy, training time, and inference speed of the models on both frameworks. 6. **User Interface**: Create a simple command-line interface where users can select which model and dataset they want to work with, choose the backend framework, and see the results of the evaluation. 7. **Documentation**: Provide clear documentation on how to install the necessary packages, run the tool, and interpret the results. The goal is to showcase the flexibility and ease of use provided by the 'ToTf' package while also giving developers a practical tool to compare the performance of different ML models across PyTorch and TensorFlow.