AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network activity, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to limited information about the maintainer, making it suspicious.
- Limited information about the maintainer
- Elevated metadata risk
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package appears to be newly created with limited information about the maintainer, raising some suspicion but not conclusive evidence of malice.
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: microsoft.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository microsoft/agent-governance-toolkit appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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 agent-governance-toolkit-core
Create a mini-application called 'AgentTrustMonitor' using the Python package 'agent-governance-toolkit-core'. This application will serve as a basic monitoring tool for agents within a network, focusing on ensuring their compliance with governance policies and maintaining trustworthiness. Step 1: Define the Scope - The application should monitor a set of predefined agents for any violations of established governance policies. - It should also track the overall trustworthiness of each agent over time based on their behavior and adherence to these policies. Step 2: Setup the Environment - Install the 'agent-governance-toolkit-core' package using pip. - Set up a virtual environment for your project. Step 3: Develop Core Functionality - Use the package's runtime and kernel functionalities to establish a secure communication channel between the monitoring application and the agents. - Implement a mechanism to define and enforce governance policies through the trust layer provided by the package. Step 4: Monitor Agents - Continuously monitor the agents for policy violations and record instances where policies are not adhered to. - Calculate and update a trust score for each agent based on their compliance history. Step 5: Reporting - Develop a simple reporting feature that generates periodic reports summarizing the compliance status and trust scores of all monitored agents. - Optionally, implement real-time alerts for critical violations. Suggested Features: - A user-friendly interface for adding, modifying, and removing agents from the monitoring list. - Detailed logs for every interaction between the application and the agents. - An API endpoint for integrating the monitoring data into other systems. How to Utilize 'agent-governance-toolkit-core': - Leverage the package's runtime to manage the execution context of the agents being monitored. - Use the kernel functionality to facilitate secure and controlled interactions with the agents. - Employ the trust layer to assess and enforce governance policies, ensuring that all agents comply with established rules and regulations.