aiodiscover

v3.3.1 suspicious
6.0
Medium Risk

Discover hosts by arp and ptr lookup

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high potential for credential harvesting, which significantly raises its risk level. However, there are no immediate signs of malicious activity beyond this.

  • High credential risk
  • Single-package maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No signs of code obfuscation or encoding patterns detected.
  • Credentials: Potential credential harvesting attempt detected with suspicious strings that may be used for unauthorized access.
  • Metadata: The maintainer has only one package on PyPI, which might indicate a new or less active account, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aiodiscover.readthedocs.io
  • Detailed PyPI description (6780 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in bluetooth-devices/aiodiscover
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • ipt>.example.com", "../etc/passwd", "-leading-hyphen.example.com", "trailing-
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: koston.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository bluetooth-devices/aiodiscover appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "J. Nick Koston" 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 aiodiscover 
Your task is to develop a Python-based network discovery tool called 'NetScout' which leverages the 'aiodiscover' package to identify active hosts on a local network through ARP and PTR lookups. This tool will serve as a valuable asset for network administrators who need to quickly ascertain which devices are connected to their network. Here’s a detailed breakdown of what your application should accomplish:

1. **User Input**: The application should start by prompting the user to input their local IP address or subnet. If no input is provided, it should default to the current machine's local IP.
2. **Network Discovery**: Utilize the 'aiodiscover' package to perform both ARP and PTR lookups across the specified subnet. The tool should asynchronously discover all active hosts, displaying their MAC addresses and corresponding PTR records if available.
3. **Host Information**: For each discovered host, gather additional information such as the hostname and operating system details, if possible. This could involve performing a reverse DNS lookup using the PTR record obtained from the 'aiodiscover' package.
4. **Visualization**: Present the gathered data in a user-friendly format. Consider implementing a simple command-line interface (CLI) or, for a more advanced version, a graphical user interface (GUI) that allows users to easily navigate through the discovered hosts and their details.
5. **Export Functionality**: Include an option to export the discovered network data into a CSV file for further analysis or record-keeping purposes.
6. **Error Handling**: Ensure robust error handling throughout the application to gracefully manage issues such as unreachable networks, DNS resolution failures, etc.
7. **Security Considerations**: Since the application deals with network scanning, discuss any potential security implications and how they can be mitigated (e.g., ensuring the application does not send out sensitive information).
8. **Documentation**: Provide comprehensive documentation for the application, detailing its installation process, usage instructions, and any dependencies required.

In this project, you'll primarily use the 'aiodiscover' package to handle the network discovery aspect. However, you may also integrate other Python libraries for additional functionalities like OS detection or GUI development. Your goal is to create a versatile yet straightforward tool that network administrators can rely on for quick and efficient network audits.