🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, missing maintainer information and lack of an associated GitHub repository raise concerns about its origin and maintenance.

Missing maintainer information
No associated GitHub repository

Per-check LLM notes

Network: The detected network call pattern is typical for fetching resources or making API calls and does not inherently suggest malicious activity.
Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some red flags such as missing maintainer information and no associated GitHub repository, which could indicate potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

e[bytes, str]: async with httpx.AsyncClient(follow_redirects=True) as client: try: r

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 LLM-Bridge

Create a multimedia content summarizer app called 'MultimediaSummarizer' that leverages the capabilities of the LLM-Bridge package. This app will accept various types of input media files (e.g., images, videos, audio clips), process them, and generate a concise summary or description of the content. Here’s how you would go about building it:

1. **Project Setup**: Begin by setting up your development environment with Python and installing the necessary packages including LLM-Bridge. Ensure that all dependencies for handling different media types are also installed.

2. **User Interface Design**: Design a simple yet user-friendly interface where users can upload their media files. This could be a web-based interface or a desktop application depending on your preference.

3. **Media Processing**: Use LLM-Bridge to interact with large language models capable of understanding and generating summaries from multimedia inputs. Ensure that the app can handle multimodal inputs, meaning it should be able to process both visual and auditory data simultaneously if needed.

4. **Summary Generation**: Once the media file is processed, use LLM-Bridge to generate a textual summary of the content. The summary should capture key points or descriptions relevant to the media type.

5. **Output Display**: Finally, display the generated summary back to the user through the same interface they used to upload the file. Optionally, allow users to download the summary as a text file or share it directly via email or social media.

**Suggested Features**:
- **Multimedia Support**: Ensure the app supports at least two different types of media files (e.g., images and videos).
- **Real-time Feedback**: Provide real-time feedback to the user during the processing phase, such as progress bars or estimated time remaining.
- **Customizable Outputs**: Allow users to specify certain parameters for the summary generation, like length or style.
- **Error Handling**: Implement robust error handling mechanisms to deal with issues like unsupported file formats or network errors.

In utilizing LLM-Bridge, focus on its ability to seamlessly integrate with different LLM providers and its support for multimodal I/O. This will enable the app to handle complex tasks efficiently while providing a consistent user experience.