🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its potential for executing shell commands and some level of code obfuscation, which may indicate hidden functionality. While there are no clear signs of malicious intent, the combination of these factors raises concerns about potential supply-chain attacks.

High shell execution risk
Moderate obfuscation risk

Per-check LLM notes

Network: Network calls may be legitimate if the package requires API interactions, but unusual endpoints or excessive data transfer should be investigated.
Shell: Executing shell commands can be risky as it may indicate the package is performing actions on the user's system without explicit permission, which could be a sign of malicious behavior.
Obfuscation: The observed pattern could indicate an attempt to obscure code logic, but without more context it's hard to determine if it's malicious.
Credentials: No obvious signs of credential harvesting detected.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

fails. """ response = requests.get(API_VERSION_URL, timeout=TIMEOUT) response.raise_for_sta
while url: response = requests.get(url, timeout=TIMEOUT) response.raise_for_status()

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

i in range(max(layers) + 1)]).eval() # Weights for each selected layer loss; default:

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

ue, run_dir, idx) proc = subprocess.run(cmd, capture_output=True, text=True) # print output

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: techfak.uni-bielefeld.de>

✓ 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 AutoForge

Develop a creative mini-application called 'Image2Print' which leverages the AutoForge Python package to convert user-uploaded images into 3D printable layered models. This application should allow users to upload any image of their choice, apply various artistic filters to enhance the design, and then generate a 3D model file suitable for printing on a 3D printer.

Step-by-Step Guide:
1. User Interface: Design a simple and intuitive web interface where users can upload an image.
2. Image Processing: Implement functionality to apply different artistic filters to the uploaded image using standard Python libraries like Pillow.
3. Model Generation: Utilize AutoForge to convert the processed image into a 3D model. Users should be able to specify parameters such as layer thickness, material type, etc., which will influence the final 3D model output.
4. Download Option: Provide an option for users to download the generated 3D model file (.stl format).
5. Preview Functionality: Before downloading, allow users to preview their 3D model in a basic 3D viewer embedded within the application.

Suggested Features:
- Multiple filter options including grayscale, sepia, negative, and custom filters.
- Adjustable parameters for 3D model generation such as resolution, scale, and layer height.
- A gallery section showcasing examples of successful conversions from image to 3D print.
- Basic user account creation allowing users to save and revisit their designs.

How AutoForge is Utilized:
- AutoForge will be used as the core engine to transform the processed image into a 3D model. Specifically, after applying filters and setting preferences, the application will call upon AutoForge functions to generate the 3D model based on the input image data. The output will be a structured 3D model file that can be directly sent to a 3D printer or viewed in 3D modeling software.