🤖 AI Analysis

Final verdict: SAFE

The package shows low risk in network and shell activities, with metadata indicating it's new and lacking detailed maintainer information. Overall, there is insufficient evidence to suggest a supply-chain attack.

Low network and shell risks
New package with incomplete metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
Shell: No shell execution patterns detected, indicating no suspicious command-line operations.
Metadata: The package is new and lacks detailed maintainer information, raising some suspicion but not conclusive evidence of 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: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/RAMPART appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 RAMPART

Your task is to develop a small but impactful application that leverages the 'RAMPART' package to ensure the safety and reliability of an agentic AI system within a controlled environment. This application will serve as a safety checker for a simple chatbot AI, ensuring it adheres to predefined safety protocols during its operation. Here’s a step-by-step guide on how to proceed:

1. **Setup**: Begin by setting up your Python development environment. Ensure you have pytest installed, as RAMPART is a pytest-native safety testing framework.
2. **Chatbot Creation**: Develop a basic chatbot using any preferred library (e.g., ChatterBot). The chatbot should be capable of engaging in simple conversations based on predefined rules and data.
3. **Safety Protocols Definition**: Define a set of safety protocols for your chatbot. These could include avoiding certain topics, ensuring positive language, and handling sensitive information appropriately.
4. **Integration with RAMPART**: Integrate RAMPART into your project. Use RAMPART to create tests that check if your chatbot complies with the defined safety protocols. For example, you can write tests to ensure the chatbot does not engage in harmful or inappropriate discussions.
5. **Testing and Validation**: Implement a series of test scenarios to validate the effectiveness of your safety checks. Run these tests using pytest to see how well your chatbot adheres to the safety protocols.
6. **User Interface**: Optionally, create a simple user interface (UI) where users can interact with the chatbot and observe the safety checks in real-time. This UI can display the conversation history and highlight any instances where the chatbot might have violated safety protocols.
7. **Documentation**: Document your setup process, including how you integrated RAMPART and how you structured your safety tests. Provide clear instructions on how to run the tests and interpret their results.

This project aims to demonstrate the practical application of RAMPART in ensuring the safe operation of agentic AI systems, providing a foundational understanding of safety testing in AI.