🤖 AI Analysis

Final verdict: SUSPICIOUS

The package aar-manifest v1.0.0 exhibits low risk in terms of network calls, shell execution, and obfuscation but has incomplete metadata, raising suspicion about its origin and purpose.

Incomplete repository information
Lack of detailed maintainer information

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being potentially malicious due to lack of repository and incomplete maintainer information.

🔬 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: ai-manifests.org>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 aar-manifest

Create a Python-based mini-application named 'AgentTracker' which leverages the 'aar-manifest' package to manage and analyze Agent Acknowledgment Records (AARs). This application will serve as a tool for tracking and understanding interactions between agents within a system. Here’s a detailed plan on how to build this application:

1. **Project Setup**: Start by setting up a new Python virtual environment and installing the 'aar-manifest' package using pip.
2. **Core Functionality**:
   - Implement functions to parse AAR files into structured data that can be easily manipulated and queried.
   - Develop a feature to generate summary reports from AAR data, highlighting key metrics such as interaction frequency, agent performance, and error rates.
3. **Database Integration**:
   - Integrate a simple SQLite database to store parsed AAR data for long-term analysis and reporting.
4. **User Interface**:
   - Design a basic command-line interface (CLI) that allows users to input AAR file paths, view summaries, and export reports.
5. **Advanced Features**:
   - Include a feature to detect anomalies in AAR data, such as unusually high error rates or unexpected interactions.
   - Implement a logging mechanism to track application usage and errors.
6. **Testing and Documentation**:
   - Write unit tests to ensure all functionalities work as expected.
   - Create comprehensive documentation detailing how to install, configure, and use 'AgentTracker'.
7. **Deployment**:
   - Package 'AgentTracker' as a standalone executable using tools like PyInstaller.
   - Provide instructions on deploying the application on different operating systems.

This project will demonstrate the practical application of the 'aar-manifest' package in real-world scenarios, offering insights into agent interactions through detailed analysis and visualization of AAR data.