AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of network activity, shell execution, obfuscation, and credential handling. However, the metadata risk score is high due to its newness and lack of detailed information, raising suspicion.
- High metadata risk score
- Minimal package details provided
Per-check LLM notes
- Network: No network calls suggest the package is not attempting to communicate externally without reason.
- Shell: No shell executions indicate the package does not execute system commands which could pose a risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is newly created with minimal information and could be suspicious.
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
Email domain looks legitimate: izusoft.tech>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository created very recently: 4 day(s) ago (2026-06-01T19:52:23Z)
Repository created very recently: 4 day(s) ago (2026-06-01T19:52:23Z)
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packagePackage is very new: uploaded 2 day(s) agoAuthor 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 aaep-maf-producer
Your task is to create a Python-based mini-application that integrates with the AAEP (Assurance Application Engagement Platform) using the 'aaep-maf-producer' package. This application will serve as a bridge between AAEP and a custom-built agent framework on top of Microsoft's Agent Framework. The goal is to demonstrate how agents can be managed, deployed, and monitored through AAEP's API capabilities. Step 1: Set up your development environment. Ensure you have Python installed along with the 'aaep-maf-producer' package. If not already installed, you can install it via pip. Step 2: Design your application architecture. Your app should include modules for initializing the connection to AAEP, deploying agents, and monitoring their status. Step 3: Implement the initialization module. This module should handle authentication with AAEP and establish a secure connection. Utilize the 'aaep-maf-producer' package to manage these interactions efficiently. Step 4: Develop the deployment module. This part of the application should allow users to upload agent configurations to AAEP. Use the 'aaep-maf-producer' package to facilitate this process. Consider adding features like version control for agent configurations and rollback mechanisms. Step 5: Create the monitoring module. This section should provide real-time status updates on deployed agents. Integrate 'aaep-maf-producer' functionalities to fetch and display relevant data such as agent health, performance metrics, and error logs. Suggested Features: - User-friendly interface for managing agent configurations. - Detailed logging and reporting capabilities. - Support for multiple deployment environments (e.g., staging, production). - Automated testing and validation of agent configurations before deployment. Ensure your application is well-documented and includes examples or tutorials for end-users. Additionally, consider writing unit tests for critical components to ensure reliability and maintainability.