🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some suspicious characteristics, particularly concerning network and shell risks, and has incomplete metadata which raises concerns about its legitimacy and origin.

Network risk indicates potential external communication
Shell risk suggests possible local command execution
Incomplete author metadata

Per-check LLM notes

Network: Network calls suggest the package is designed to communicate externally, possibly for legitimate purposes like API interactions.
Shell: Shell execution patterns might indicate the package executes commands locally, which could be part of its functionality, but requires further investigation to confirm legitimacy.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The author's name is missing or very short, and the author seems to be new or inactive.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

encode("utf-8") req = urllib.request.Request( self.url, data=body,
POST", ) with urllib.request.urlopen(req, timeout=self.timeout) as resp: resp
Currently patches: - `urllib.request.OpenerDirector.open` (covers `urlopen`) - `requests.ad
).encode("utf-8") req = urllib.request.Request( OLLAMA_URL, data=body, head
method="POST", ) with urllib.request.urlopen(req, timeout=120) as resp: return json.loads

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

user_id="alice"): subprocess.run(["true"], check=False, capture_output=True) hook_eve
res but does not emit subprocess.run(["true"], check=False, capture_output=True) hook_eve

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aegrail/aegrail appears legitimate

⚠ 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 aegrail

Develop a mini-application called 'AgentMonitor' using the Python package 'aegrail'. This application will serve as a monitoring tool for AI agents in production environments, allowing users to track agent performance, health, and compliance with runtime contracts. Here are the key steps and features to include in your project:

1. **Setup**: Begin by installing 'aegrail' and setting up a basic structure for your application. Ensure you have a main module where the application logic will reside.
2. **Agent Registration**: Implement a feature where users can register their AI agents. Each registration should include essential details such as the agent's name, type of AI model it uses, and its primary function.
3. **Health Check**: Utilize 'aegrail' to perform regular health checks on registered agents. These checks should verify if the agents are running, responding to requests within expected time limits, and not exceeding resource usage thresholds.
4. **Performance Metrics**: Integrate functionality to collect and display performance metrics for each agent. This could include response times, accuracy rates, and error rates.
5. **Compliance Verification**: Use 'aegrail' to enforce and monitor compliance with specified runtime contracts. Contracts define acceptable behavior, data handling practices, and security protocols that agents must adhere to.
6. **User Interface**: Develop a simple web interface where users can view the status of their agents, including health check results and performance metrics. Users should also be able to trigger manual health checks from this interface.
7. **Notifications**: Implement a system to send notifications (e.g., via email or SMS) when an agent fails a health check or violates a runtime contract.
8. **Documentation**: Provide comprehensive documentation for both end-users and developers. Include setup instructions, API references, and best practices for maintaining agent compliance.

The goal is to create a robust, user-friendly application that leverages 'aegrail' to ensure AI agents operate efficiently and safely in production environments.