🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of malicious activities such as network calls, shell executions, or obfuscation. However, the metadata risk score is elevated due to the non-existent repository and the maintainer having only one package, which raises concerns about potential supply-chain attacks.

No network calls detected
Repository not found
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is generally safe.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository is not found and the maintainer has only one package, which may indicate a new or less active account, raising some suspicion.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "The AgentForge Authors" 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 agentforge-guard-llamaguard

Create a mini-application named 'ContentGuardian' that utilizes the 'agentforge-guard-llamaguard' package to classify and filter text content based on its suitability. This application will serve as a tool for users to input text snippets and receive a classification indicating whether the text is safe, potentially harmful, or requires human review. The primary goal is to demonstrate how 'agentforge-guard-llamaguard' can be integrated into real-world applications for content moderation purposes.

**Steps to Create ContentGuardian:**
1. **Set Up Your Environment**: Begin by setting up your Python environment. Ensure you have Python installed, then install the required packages including 'agentforge-guard-llamaguard'.
2. **Design the User Interface**: Develop a simple command-line interface (CLI) where users can input their text snippets. This CLI should be user-friendly and provide clear instructions on how to use the application.
3. **Integrate 'agentforge-guard-llamaguard'**: Utilize 'agentforge-guard-llamaguard' to analyze the input text and classify it according to predefined categories such as 'safe', 'potentially harmful', and 'requires human review'. The application should output the classification result back to the user.
4. **Implement Additional Features**: Consider adding extra functionalities like saving classified texts to a file for later review, providing detailed explanations for each classification, and allowing users to set custom thresholds for what constitutes 'potentially harmful' content.
5. **Testing and Validation**: Test your application thoroughly using various types of text inputs to ensure accuracy and reliability of classifications.
6. **Documentation**: Write clear documentation explaining how to run the application, what 'agentforge-guard-llamaguard' does, and how it contributes to the functionality of ContentGuardian.

By following these steps, you'll create a practical, functional mini-application that showcases the power of 'agentforge-guard-llamaguard' in moderating online content.