🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential network and shell execution activities, which could pose security threats if not properly managed. Additionally, incomplete metadata adds a layer of uncertainty.

moderate network risk
potential shell execution
incomplete author metadata

Per-check LLM notes

Network: The network call pattern suggests the package may be performing external API calls, which could be legitimate if documented and necessary for its functionality.
Shell: The shell execution pattern indicates the package might launch an external executable, which is potentially risky if not properly sanitized or if the executable's source is untrusted.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not definitive proof of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

aset_name) response = requests.get(url, stream=True, timeout=30) response.raise_for_sta

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

gs = gui_args[0].split() subprocess.run( # noqa: S603 [panel_executable, "serve", str(app_p

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: noc.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository paidiver/paidiverpy appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 Paidiverpy

Create a photo preprocessing tool using the 'Paidiverpy' Python package. This tool will serve as a versatile utility for photographers and developers who need to prepare their images for machine learning models or other applications. The application should include the following functionalities:

1. **Image Upload**: Users should be able to upload one or multiple images directly from their device or via a URL.
2. **Preprocessing Options**: Implement various preprocessing techniques such as resizing, cropping, normalization, and color correction. Each option should have adjustable parameters to allow customization.
3. **Batch Processing**: The tool should support batch processing of multiple images simultaneously, applying the same preprocessing steps to all selected files.
4. **Preview Before Saving**: Provide a preview feature where users can see the changes applied to each image before saving the processed version.
5. **Saving Processed Images**: Once satisfied, users should be able to save the processed images either back to their device or upload them to a cloud storage service like AWS S3 or Google Cloud Storage.
6. **Documentation and Help**: Include comprehensive documentation and tooltips within the application to guide users through the available options and features.

The 'Paidiverpy' package will be utilized for the core image preprocessing tasks. Specifically, you will use its functions to apply the necessary transformations to the uploaded images. Additionally, consider integrating 'Paidiverpy' with other libraries such as PIL or OpenCV for additional functionality. Ensure the application is user-friendly and efficient, making it easy for both beginners and experienced users to enhance their image datasets.