🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to shell execution and low maintainer effort, which raises concerns about its legitimacy and security practices.

Shell risk: Potential misuse of shell commands.
Metadata risk: Lack of proper repository and low maintainer effort.

Per-check LLM notes

Network: No network calls were detected.
Shell: Shell execution is present and could indicate potential risks if not properly justified within the package's functionality.
Obfuscation: The observed patterns are likely related to model evaluation and inference processes rather than obfuscation.
Credentials: No suspicious patterns for credential harvesting were identified.
Metadata: The package shows signs of low maintainer effort and lacks a proper GitHub repository, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

self.model.eval() for data in valid_dataloader:
e: self.model.eval() with torch.no_grad():
self.model.eval() with torch.no_grad():
self.classifier.eval() with torch.no_grad():

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

bar.update(1) os.system(f"ollama stop {self.model}") def dynamic_hierarchical_s
bar.update(1) os.system(f'ollama stop {self.model}') def __call__(self, paths:l
self.convo = {} os.system(f'ollama stop {self.model}') def multi_turn(self,
pdate(1) os.system(f'ollama stop {self.model}') from .multi_turn_vllm import v

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ 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 Cirilla

Create a small machine learning application using the Python package 'Cirilla' that focuses on optimizing single-GPU training for a specific task. This application will be designed to train a neural network model on a dataset of your choice, showcasing Cirilla's capabilities in enhancing training efficiency. The app should include the following features:

1. **Data Loading and Preprocessing**: Implement functionality to load a dataset, preprocess it (normalization, augmentation, etc.), and split it into training and validation sets.
2. **Model Definition**: Define a neural network architecture suitable for the chosen task (e.g., image classification, object detection).
3. **Training Loop**: Utilize Cirilla to optimize the training process on a single GPU. Include real-time performance metrics such as loss and accuracy.
4. **Evaluation**: After training, evaluate the model on the validation set and output performance metrics.
5. **Visualization**: Provide visualizations of the training progress, including loss and accuracy curves over epochs.
6. **Documentation**: Write clear documentation explaining each part of the code and how Cirilla enhances the training process.

Use Cirilla's core functionalities to streamline the training phase, ensuring that the application demonstrates significant improvements in training speed or resource utilization compared to standard methods. Additionally, include a brief comparison analysis between traditional training methods and Cirilla-optimized training to highlight the benefits of using Cirilla.