agentkeyring

v0.1.0 suspicious
6.0
Medium Risk

Python client for AgentVault — AI-native credential vault for autonomous agents.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits suspicious behavior due to unusual metadata activity, including rapid commits and a lack of maintainer history. While network, shell, obfuscation, and credential risks are relatively low, the metadata risk is high, warranting further investigation.

  • High metadata risk due to unusual activity patterns.
  • Low risk in terms of network, shell, obfuscation, and credential harvesting.
Per-check LLM notes
  • Network: Network calls indicate external communication which could be legitimate but requires verification of purpose and destination.
  • Shell: No shell execution patterns detected, suggesting low risk of direct command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows unusual activity indicative of potential malicious intent, such as rapid commits and lack of maintainer history.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • ential value.""" with httpx.Client(timeout=self._timeout) as c: resp = c.post(
  • o this agent.""" with httpx.Client(timeout=self._timeout) as c: resp = c.get(f"{sel
  • ault": self._client = httpx.AsyncClient(timeout=self._timeout) return self async def __
✓ 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 score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 8 commits happened within 24 hours
⚠ 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 agentkeyring 
Create a Python-based mini-application named 'AgentSecure' that integrates the 'agentkeyring' package to manage secure credentials for autonomous agents. This application will serve as a user-friendly interface for storing, retrieving, and managing sensitive information such as API keys, passwords, and other secrets in a secure manner. Here are the steps and features to include in your project:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing the 'agentkeyring' package alongside any other necessary dependencies.
2. **User Authentication**: Implement a simple user authentication system where users can create accounts and log in to access their credential vaults. Use 'agentkeyring' to securely store user credentials.
3. **Credential Management**: Allow users to add new credentials (such as API keys, database passwords), update existing ones, and delete them if no longer needed. Utilize 'agentkeyring' functions to interact with the AgentVault service for these operations.
4. **Retrieve Credentials**: Provide functionality for users to retrieve stored credentials by specifying the type of credential they need (e.g., API key for a specific service).
5. **Security Enhancements**: Implement additional security measures such as encryption of data at rest, two-factor authentication for critical actions, and logging of all credential changes for audit purposes.
6. **User Interface**: Develop a command-line interface (CLI) for interacting with 'AgentSecure', making it easy for users to perform all credential management tasks from the terminal.
7. **Documentation**: Write comprehensive documentation explaining how to install and use 'AgentSecure', including examples of common use cases and best practices for securing credentials.

By following these steps and incorporating the 'agentkeyring' package effectively, you'll develop a robust, secure, and user-friendly application for managing credentials in a modern, AI-driven environment.