agentixx

v0.2.6 suspicious
5.0
Medium Risk

Sandboxed rollouts you call like typed Python (import as `agentix`)

🤖 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.

💬 Discussion Feed

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