agent-action-guard

v1.1.5 suspicious
4.0
Medium Risk

Runtime classifier for screening AI agent actions as safe, harmful, or unethical.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks across network, shell, obfuscation, and credential fronts, but the incomplete metadata and potentially inactive maintainer raise concerns about its legitimacy and long-term support.

  • Incomplete maintainer information
  • Potentially inactive maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution detected, indicating the package does not execute system commands directly.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing and the account seems new or inactive, which raises some suspicion but does not conclusively indicate malice.

🔬 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

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Pro-GenAI/Agent-Action-Guard appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 agent-action-guard 
Create a mini-application called 'EthicalGuard' which acts as a runtime monitor for AI agents to ensure their actions adhere to ethical guidelines. This application will utilize the 'agent-action-guard' package to classify actions as safe, harmful, or unethical. Here are the steps and features you need to implement:

1. **Setup**: Begin by setting up your Python environment and installing the 'agent-action-guard' package.
2. **Agent Interaction**: Develop a simple AI agent (e.g., a chatbot or decision-making algorithm) that performs various actions based on user inputs or predefined scenarios.
3. **Action Classification**: Use 'agent-action-guard' to evaluate each action taken by the agent. The application should output whether the action is classified as safe, harmful, or unethical.
4. **Feedback Mechanism**: Implement a feedback mechanism where users can report if they believe an action was incorrectly classified. This feedback will help improve the accuracy of the classification model over time.
5. **Reporting Dashboard**: Create a basic reporting dashboard within the application that displays statistics on the types of actions performed and their classifications. This could include graphs showing trends in ethical behavior over time.
6. **Customization Options**: Allow users to customize the ethical guidelines used by 'agent-action-guard', so the application can adapt to different contexts or industries.
7. **Integration with External Systems**: Demonstrate how EthicalGuard can integrate with external systems or APIs (such as chat platforms or decision support systems) to monitor and control AI actions in real-time.
8. **Documentation & Tutorial**: Provide comprehensive documentation and a step-by-step tutorial on how to use EthicalGuard effectively, including best practices for integrating it into existing AI projects.