🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network calls, shell execution, obfuscation, and credential harvesting. However, its metadata suggests it's a new package with low activity, raising some suspicion.

New package with low activity
Low risks in other categories

Per-check LLM notes

Network: The network call pattern suggests legitimate HTTP requests, possibly for updating or fetching resources.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package is new with low activity, which raises some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

Test runner config found: pyproject.toml
9 test file(s) detected (e.g. test_agent_signer.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6590 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

36 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 31 commits in notenkitoclient-cpu/agentpass
Single author but highly active (31 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: async with httpx.AsyncClient(timeout=timeout_cfg) as client: async with c

✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "AgentPass Contributors" 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 agentpass-ai

Develop a secure, autonomous chatbot named 'AgentGuard' using the 'agentpass-ai' package. This chatbot will serve as a customer service representative for a fictional tech company, providing support and information to users while ensuring that all interactions are replay-safe and authenticated. Here’s a detailed breakdown of the project requirements and steps:

1. **Setup Environment**: Begin by setting up your Python environment. Install necessary packages including 'agentpass-ai'. Ensure you have the latest version of 'agentpass-ai' installed.
2. **Design the Chat Interface**: Create a simple text-based chat interface where users can type their queries and receive responses from AgentGuard. For simplicity, you can use a console application or a basic web interface.
3. **Implement Authentication Middleware**: Use 'agentpass-ai' to implement replay-safe authentication for each interaction. This ensures that no message can be replayed to trick the system into repeating its response or performing actions based on outdated requests.
4. **Integrate Knowledge Base**: Integrate a knowledge base where AgentGuard can fetch answers to common questions. This could be a database or a set of predefined responses.
5. **Develop Autonomous Responses**: Program AgentGuard to provide relevant and informative responses based on user inputs. Utilize natural language processing techniques to understand and respond accurately to user queries.
6. **Security Enhancements**: Implement additional security measures such as rate limiting and IP blocking to prevent abuse and ensure the integrity of the chat sessions.
7. **Testing and Deployment**: Thoroughly test the application to ensure it functions correctly and securely. Deploy the application in a safe environment for public use or internal testing.

**Features to Consider**:
- User-friendly interface for easy interaction.
- Detailed logging of interactions for auditing purposes.
- Customizable responses based on user preferences or history.
- Integration with external APIs for more dynamic responses.

By utilizing 'agentpass-ai', your application will not only provide valuable customer support but also demonstrate advanced security practices in handling autonomous AI interactions.