🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk level due to its capability to execute shell commands, which poses a significant security risk if misused. However, there are no indications of malicious intent or credential harvesting.

High shell execution risk
Potential for misuse in executing external commands

Per-check LLM notes

Network: The network calls appear to be for making HTTP requests to an API endpoint and testing internet connectivity, which could be legitimate for certain functionalities.
Shell: The shell execution patterns indicate potential for running external commands, including Docker commands, which can be risky if not properly controlled and may pose a significant security risk if used improperly.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious flags are present.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

on( """ import socket socket.create_connection(("example.com", 80), timeout=1) """ ) assert result
ools=tools) request = urllib.request.Request( url=f"{self.base_url}/chat/completions"
try: with urllib.request.urlopen(request, timeout=self.timeout_seconds) as response:
) monkeypatch.setattr("urllib.request.urlopen", fake_urlopen) model = QwenModelAdapter(api_ke

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

try: completed = subprocess.run( command, stdin=subprocess.D
try: completed = subprocess.run( [self.docker_executable, "rm", "-f", contai
try: completed = subprocess.run( command, cwd=workspace,
try: completed = subprocess.run( command, stdin=subprocess.DEVNULL,

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Agent Playground contributors" 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-playground

Create a fully-functional mini-application named 'AgentArena' that allows users to experiment with different AI agents in a controlled environment using the Python package 'agent-playground'. This application should enable users to upload their own simple AI scripts (written in Python) which will then compete against each other in various challenges or games. Each game will have its own set of rules defined within the application, ensuring that all uploaded agents can interact with the game environment seamlessly.

Key Features:
- User Interface: Develop a clean and intuitive web-based interface where users can upload their AI scripts and select which game/challenge they want their agents to participate in.
- Sandbox Execution: Utilize the 'agent-playground' package to safely execute user-uploaded Python code in a sandboxed environment, preventing any malicious actions from affecting the host system.
- Game Variety: Include at least three different types of games/challenges such as a simple puzzle game, a basic text-based adventure game, and a number guessing game. Each game should test different aspects of the AI agents' capabilities.
- Agent Management: Allow users to save and name their AI agents, view past performances, and compare results between different agents.
- Leaderboard: Implement a leaderboard to showcase the best-performing AI agents across all games and challenges.

The 'agent-playground' package will be utilized primarily for executing user-uploaded AI scripts in a safe and controlled manner. It will also facilitate the creation of isolated environments for each game challenge, ensuring that the agents can interact with the game logic without interference from external factors. Additionally, the package's local-first nature will ensure that all operations are performed efficiently on the user's device, enhancing performance and security.