🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential misuse of shell commands and lack of detailed metadata, though it does not strongly suggest a supply-chain attack.

Moderate network risk with benign HTTP requests.
High shell risk due to potential misuse of commands like 'docker' and 'git'.
Low metadata risk indicating possible inexperience of the author.

Per-check LLM notes

Network: The network calls are primarily for making HTTP requests and don't indicate immediate malicious behavior.
Shell: The shell execution patterns include running commands like 'docker' and 'git', which could potentially be used for more than just local development purposes, suggesting higher risk.
Metadata: The author has only one package and lacks PyPI classifiers, suggesting low effort or inexperience.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

se_url self._client = httpx.AsyncClient(base_url=base_url, timeout=timeout) # Socket.IO book
pp=server.app) async with httpx.AsyncClient(transport=transport, base_url="http://test") as http:

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

""" fn = pickle.loads(base64.b64decode(self.encode("ascii"))) if not callable(fn):
") return pickle.loads(raw) if raw is not None else None if kind ==
y: args, kwargs = pickle.loads(request.arguments) except Exception as exc:
hon callable.""" fn = pickle.loads(base64.b64decode(self.encode("ascii"))) if not calla
nd-trip back to fn assert pickle.loads(r.arguments) == ((1, 2), {"k": "v"}) def test_remote_calla
resp.error is None assert pickle.loads(resp.value) == {"x": 1} def test_remote_response_error_sha

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

, file=sys.stderr) proc = subprocess.run(cmd, cwd=cwd) if proc.returncode != 0: raise Sys
, file=sys.stderr) proc = subprocess.run( f"{result} | docker load", shell=True,
= f"{name}:latest" proc = subprocess.run( ["docker", "tag", loaded, alias], capture_o
th everything staged. subprocess.run( ["git", "init", "-q"], cwd=stage,
check=True, ) subprocess.run( ["git", "add", "-A"], cwd=stage,
uilder.nix`). Worker code # (`subprocess.run("claude", ...)`, `c.remote(cc.run, ...)`, ...) must # be abl

✓ 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 Agentiix/Agentix appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Agentiix" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentixx

Create a mini-application named 'SecureCodeRunner' using the Python package 'agentixx'. This application should allow users to securely execute user-submitted Python code snippets within a sandboxed environment while providing feedback on the execution results. The goal is to demonstrate how 'agentixx' can safely handle untrusted code inputs.

Step 1: Initialize the Project
- Set up a new Python virtual environment and install 'agentixx'.

Step 2: Design the User Interface
- Create a simple command-line interface (CLI) where users can input their Python code snippets.
- Implement a feature to display execution time and any exceptions caught during the code execution.

Step 3: Implement Code Execution
- Utilize 'agentixx' to define a sandboxed environment for executing the user-provided code.
- Ensure that the sandboxed execution prevents access to sensitive system resources and restricts the code's runtime to a safe limit.
- Provide options for users to specify basic data types and simple variables as inputs to the executed code.

Step 4: Add Advanced Features
- Allow users to choose from predefined categories of Python packages that can be imported into the sandboxed environment (e.g., math, datetime).
- Introduce a logging mechanism to record all executions and exceptions for later analysis.

Step 5: Test and Validate
- Write unit tests to ensure that the sandboxed environment behaves as expected and does not allow unintended access or actions.
- Validate the application by running various code snippets, including edge cases and malicious attempts, to confirm its robustness.

How 'agentixx' is utilized:
- Import 'agentixx' at the beginning of your Python files where you need to execute code in a sandboxed manner.
- Use 'agentixx' to create a sandbox context in which you pass the user's code snippet.
- Handle the output or exceptions returned by 'agentixx' to provide meaningful feedback to the user.

This project will showcase the power of 'agentixx' in providing a secure way to evaluate and execute untrusted code, making it suitable for applications like online coding challenges, educational tools, or even rudimentary web-based code editors.