AI Analysis
Final verdict: SAFE
The package appears to be a legitimate honeypot simulation tool without evident malicious activities. While there is some obfuscation risk due to dynamic imports, this does not strongly suggest malintent given the nature of the tool.
- Low network and shell execution risks
- Potential obfuscation through dynamic imports
- No evidence of credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is expected for a honeypot package that may not actively communicate.
- Shell: No shell execution patterns detected, consistent with a benign honeypot tool.
- Obfuscation: The code snippet suggests dynamic import which could be used for obfuscation but might also serve a legitimate purpose such as plugin architecture.
- Credentials: No suspicious patterns related to credential harvesting were detected.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not enough for high concern.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
try: output = __import__('output_plugins.{}'.format(engine), globals(), locals(), ['ou
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: yahoo.com
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 "Vesselin Bontchev" 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 adbhoneypot
Your task is to develop a security monitoring tool named 'ADBGuard' using the Python package 'adbhoneypot'. This tool will act as a honeypot to simulate an Android Debug Bridge (ADB) server environment, designed to detect and log unauthorized access attempts. Here’s a detailed breakdown of the project scope and features: 1. **Project Setup**: Start by setting up your development environment with Python and installing the 'adbhoneypot' package. 2. **Simulating ADB Server**: Utilize 'adbhoneypot' to create a simulated ADB server environment. Configure it to listen on a specific port and mimic the behavior of a real ADB server. 3. **Logging Mechanism**: Implement a logging feature that records all interaction attempts made with the simulated ADB server. This includes timestamps, IP addresses, and any commands sent to the server. 4. **Alert System**: Integrate an alert system that triggers notifications (via email, SMS, or another method of your choice) whenever an unauthorized access attempt is detected. Define what constitutes an unauthorized access attempt based on patterns or frequency of requests. 5. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with ADBGuard. This interface should allow users to start and stop the honeypot, view logs, and configure settings such as alert thresholds. 6. **Security Enhancements**: Explore additional security measures that could be integrated into ADBGuard, such as encryption of logs, secure communication channels, or integration with external security tools. 7. **Testing and Validation**: Conduct thorough testing to ensure the honeypot behaves correctly and accurately detects unauthorized access. Validate its effectiveness by simulating various attack scenarios. 8. **Documentation**: Provide comprehensive documentation detailing how to install, configure, and use ADBGuard, including examples of typical use cases and troubleshooting tips. By following these steps, you'll create a robust and useful security tool that leverages the capabilities of 'adbhoneypot' to protect against unauthorized ADB access.