AI Analysis
Final verdict: SUSPICIOUS
The package shows some potential risks due to its shell execution capabilities and the lack of proper maintainer metadata, but it does not exhibit signs of malicious activity.
- Shell execution capability poses a moderate risk.
- Maintainer metadata suggests low intent or oversight.
Per-check LLM notes
- Network: No network calls detected, which is low risk.
- Shell: Shell execution capability can be risky if not properly controlled, suggesting potential for misuse.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate low intent or oversight.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
}"}) try: proc = subprocess.Popen( cmd, cwd=str(root), env=env, stdout
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: novalitix.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository brellsanwouo/agentlantern appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 agentlantern
Create a Python-based application named 'LLMInspector' that leverages the 'agentlantern' package to document and inspect Large Language Model (LLM) agent systems. The application should serve as a comprehensive tool for developers and researchers working with LLMs. Here are the detailed steps and features of the application: 1. **Setup and Initialization**: Start by installing the 'agentlantern' package via pip and setting up your Python environment. Ensure that you have access to at least one LLM API for testing purposes. 2. **Configuration Interface**: Develop a simple configuration interface where users can input details about their LLM system, such as the model name, API endpoint, and any specific parameters needed for interaction. 3. **Documentation Generation**: Implement a feature that automatically generates documentation for the LLM system based on its structure and capabilities using 'agentlantern'. This documentation should include an overview, technical specifications, and usage examples. 4. **System Inspection Tools**: Utilize 'agentlantern' to create tools that allow users to inspect various aspects of the LLM system in real-time. This could include performance metrics, response latency, and memory usage. 5. **Interactive Query Testing**: Allow users to interactively test queries against the LLM system through the application. Display the responses along with detailed metadata provided by 'agentlantern', such as the confidence score and time taken to generate the response. 6. **Customization Options**: Provide customization options for the inspection tools and query tests, enabling users to tailor the application to their specific needs. 7. **Report Generation**: Integrate a feature that compiles all the inspection data into a report format, which can be exported as a PDF or HTML file. This report should summarize the findings from the system inspection and query tests. 8. **User Interface**: Design a user-friendly GUI or CLI interface for the application, ensuring that it is accessible and easy to navigate. Throughout the development process, make sure to leverage the core functionalities of 'agentlantern' to enhance the application's capabilities and ensure accurate and reliable documentation and inspection of LLM systems.