🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to high shell execution risk and network interaction, which could potentially lead to unintended behaviors or vulnerabilities. However, there is no evidence of obfuscation or credential harvesting.

High shell execution risk
Moderate network interaction risk

Per-check LLM notes

Network: Network calls could be part of legitimate functionality, but should be scrutinized for unexpected external interactions.
Shell: Shell execution poses higher risk due to potential command injection vulnerabilities and unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The maintainer has only one package, suggesting a new or less active account which could be risky but not conclusive.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

[:-3] try: resp = requests.get(URL, timeout=TIMEOUT) if resp.status_code == 200:
ession self.session = requests.Session() self.session.headers.update( {
.[/dim]") response = requests.get(url, headers=headers, params=params, timeout=30) if
.[/dim]") response = requests.get(url, headers=headers, timeout=10) if verbose and co
try: response = requests.delete(url, headers=headers, timeout=30) response.raise_for
"} response = requests.put(oss_url, data=tar, headers=headers) response

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

{' '.join(cmd)}") return subprocess.run(cmd, capture_output=True, text=True, timeout=60, check=check
try: result = subprocess.run( [sys.executable, "-m", "cli.cli", "build",

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository inclusionAI/AEnvironment appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AEnvironment Team" 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 aenvironment

Create a fully-functional mini-application named 'AIEnvExplorer' using the AEnvironment Python SDK. This application will serve as a tool for developers and researchers to explore and manage AI environments efficiently. The app should allow users to create, delete, and manage AI environments tailored for training and testing AI agents. Here’s a detailed breakdown of the steps and features to implement:

1. **Setup Environment**: Start by installing the required packages including AEnvironment SDK.
2. **User Interface**: Develop a simple command-line interface (CLI) or a basic web interface using Flask for ease of use.
3. **Environment Management**: Implement functionalities to create new AI environments, modify existing ones, and delete environments no longer needed.
4. **Configuration Options**: Allow users to configure various parameters within their environments such as setting up different simulation speeds, enabling/disabling certain AI tools, etc.
5. **Integration with External Tools**: Enable seamless integration with popular AI frameworks like TensorFlow or PyTorch for enhanced functionality.
6. **Logging and Monitoring**: Provide real-time logging and monitoring capabilities so users can track the performance and status of their environments.
7. **Documentation and Help**: Include comprehensive documentation and a help section within the application to assist users in understanding its full potential.

Throughout the development process, ensure that the AEnvironment SDK is utilized effectively to leverage its production-grade features for managing AI environments. Your goal is to create a robust, user-friendly application that streamlines the process of working with AI environments.