🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity such as network calls or credential harvesting. However, the metadata risk score is elevated due to the unavailability of the repository and the newness of the maintainer's account.

Metadata risk score is high due to unverified repository status and new maintainer
Low individual risk scores for network, shell, obfuscation, and credential risks

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The repository is not found and the maintainer has a new account with limited history, raising suspicion.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (11168 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
Type checker (mypy / pyright / pytype) referenced in project
10 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 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 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "yeick010" 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 agentshield-langchain

Create a Python-based chatbot application named 'VerdictBot' that integrates the 'agentshield-langchain' package to ensure secure and transparent interactions between users and the bot's backend services. VerdictBot should have the following functionalities:

1. **User Interaction**: The chatbot should be able to engage in natural language conversations with users, responding to a variety of commands and queries.
2. **Tool Call Verification**: Utilize the 'agentshield-langchain' package to emit signed verdict envelopes for every interaction with external tools or services. This ensures that each tool call is verified and secured, providing users with confidence in the integrity of their data and interactions.
3. **Customizable Responses**: Allow developers to customize responses based on specific user inputs or contexts, ensuring that the chatbot can adapt its behavior according to different scenarios.
4. **Logging and Analytics**: Implement logging capabilities to record all interactions and tool calls, which can later be analyzed for performance optimization and security audits.
5. **User Feedback Mechanism**: Incorporate a feedback system where users can rate their experience with the chatbot, helping to improve future interactions and functionalities.
6. **Integration with External Services**: Enable the chatbot to interact with various external services such as weather APIs, news feeds, or social media platforms, ensuring each interaction is secured through the 'agentshield-langchain' package.

The core of the application will involve setting up a basic conversational AI model, integrating the 'agentshield-langchain' package for secure tool call handling, and building out the necessary UI/UX elements for user interaction. Additionally, focus on making the application modular so that it can easily be extended with new features or integrations in the future.