🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential risks. However, the metadata risk score is relatively high due to missing maintainer information and lack of a GitHub repository, raising concerns about the package's reliability and potential for supply-chain attacks.

High metadata risk due to missing maintainer details and no GitHub repository
Otherwise, low risk in network, shell, obfuscation, and credential aspects

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution detected, indicating the package does not execute system commands, which is safe unless command execution is a necessary part of its functionality.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package lacks a maintainer's name and has no associated GitHub repository, indicating potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

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 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 actions-graph

Create a mini-application called 'LLM Explorer' that allows users to visualize and analyze interactions between a user and a Language Learning Machine (LLM). This application will leverage the 'actions-graph' Python package to store, track, and visualize these interactions within a Memgraph database. Here’s a detailed plan on how to proceed:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with necessary libraries such as 'actions-graph', 'pymongo', and 'flask'. Install 'actions-graph' via pip.

2. **Database Initialization**: Use 'actions-graph' to initialize a connection to your Memgraph instance. Define the schema for storing LLM actions, tool calls, and session data. This schema should include fields like action type, timestamp, input/output data, and session ID.

3. **User Interface**: Develop a simple Flask-based web interface where users can input their queries to an LLM. The application should also allow users to view past interactions and filter them based on different criteria (e.g., date range, action type).

4. **Interaction Logging**: Each interaction with the LLM should be logged using 'actions-graph'. Ensure that the logs include all relevant details from the interaction, including the context in which it occurred.

5. **Visualization Tool**: Implement a feature that visualizes the stored interactions as a graph within the Memgraph database. Users should be able to interact with this graph to explore different paths of interaction and see how various inputs and outputs relate to each other.

6. **Analysis Features**: Provide basic analysis tools that can help users understand patterns in the interactions. For example, identify common sequences of actions, frequency of certain types of queries, etc.

7. **Security and Privacy**: Ensure that the application handles user data securely. Implement measures to protect sensitive information and ensure compliance with privacy regulations.

8. **Testing and Documentation**: Thoroughly test the application to ensure it works as expected. Write comprehensive documentation explaining how to set up and use the application, including any limitations and best practices.