anton-agent

v2.26.5.29.4 suspicious
6.0
Medium Risk

Autonomous coding copilot

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors including potential misuse of network calls and shell executions, which are not adequately protected against abuse.

  • network risk due to lack of input validation
  • shell risk from direct use of os.system and subprocess.run
Per-check LLM notes
  • Network: The network calls could be part of the package's functionality, but the lack of input validation and error handling raises some concerns about potential misuse.
  • Shell: Direct use of os.system and subprocess.run can indicate risky behavior, especially if the commands are not properly sanitized or controlled, potentially allowing for arbitrary command execution.
  • Obfuscation: The use of dynamic code execution with obfuscated input is suspicious and could be used for malicious purposes.
  • Credentials: No clear patterns indicative of credential harvesting were detected.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_openai_setup.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14794 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 336 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in mindsdb/anton
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • n thread.""" try: urllib.request.urlopen(url, timeout=_TIMEOUT) except Exception:
  • h headers). """ req = urllib.request.Request(url, data=payload, method=method) req.add_header
  • ode = ssl.CERT_NONE with urllib.request.urlopen(req, context=ctx, timeout=timeout) as resp:
  • re.""" try: req = urllib.request.Request(_RELEASES_LATEST_URL, headers=_GITHUB_API_HEADERS)
  • HUB_API_HEADERS) with urllib.request.urlopen(req, timeout=2) as resp: data = json.loa
  • rl}{path}" async with aiohttp.ClientSession() as session: async with session.post(
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • scratchpad>", "exec") exec(compiled, namespace) except ModuleNotFoundError as _mnf:
  • try: exec(compiled, namespace) except Exception:
  • ] try: compiled = compile(code, "<scratchpad>", "exec") exec(compiled, namespace) except ModuleNotFou
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • policy summary panel.""" os.system("cls" if sys.platform == "win32" else "clear") logo = "
  • o onboarding starts fresh os.system("cls" if sys.platform == "win32" else "clear") app = typer
  • ) result = subprocess.run( [uv, "pip", "install", "--python", sys.exec
  • "Darwin": return subprocess.run( ["pbpaste"], capture_output
  • "Windows": return subprocess.run( ["powershell", "-Command", "Get-Clipboard"]
  • aste": return subprocess.run( ["wl-paste", "--no-newline"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mindsdb.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository mindsdb/anton appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 anton-agent 
Create a Python-based code completion and debugging assistant named 'CodeMentor'. This tool will utilize the 'anton-agent' package to provide intelligent suggestions and corrections for Python code snippets. The application should have a user-friendly interface where developers can input their code, receive real-time feedback, and get suggestions for improvements or bug fixes.

Step 1: Set up a basic Python environment and install the 'anton-agent' package.
Step 2: Design a simple GUI using a library like Tkinter for the user interface.
Step 3: Implement functionality within the GUI to allow users to paste their Python code.
Step 4: Use the 'anton-agent' package to analyze the pasted code and provide immediate feedback on syntax errors, logical issues, or potential improvements.
Step 5: Display the feedback from 'anton-agent' directly in the GUI for easy visibility.
Step 6: Add an option for users to request specific types of suggestions, such as performance optimization tips or code refactoring ideas.
Step 7: Integrate a feature that allows users to save their code along with the suggested improvements into a separate file or database.

Suggested Features:
- Syntax highlighting in the code editor.
- A history of previous inputs and outputs for quick reference.
- Option to share feedback or saved files via email or download.
- Integration with popular Python libraries to offer more context-specific suggestions.

How 'anton-agent' is Utilized:
'anton-agent' acts as the brain behind CodeMentor, processing the inputted code to identify issues and suggest solutions. Its autonomous nature makes it capable of understanding complex code structures and providing meaningful insights without extensive manual configuration.

💬 Discussion Feed

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