arkvoid

v1.0.0 suspicious
4.0
Medium Risk

Official Python SDK for ARKVOID – AI Agent Monitoring & Governance

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct execution and network calls, but its metadata raises concerns due to lack of maintainer details and being newly released.

  • Low risk in network, shell, obfuscation, and credential handling
  • Metadata risk due to new package and insufficient maintainer information
Per-check LLM notes
  • Network: The network call patterns are typical for making HTTP requests and don't inherently suggest malicious activity.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The package is new and lacks detailed maintainer information, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/arkvoidai/arkvoid/tree/main/sdk/python
  • Detailed PyPI description (5962 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
  • 58 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in arkvoidai/arkvoid
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • encode("utf-8") req = urllib.request.Request(url, data=data, headers=self._headers, method="POST"
  • try: with urllib.request.urlopen(req, timeout=self._timeout) as resp:
  • encode("utf-8") req = urllib.request.Request( url, data=data, headers=self._headers,
  • try: with urllib.request.urlopen(req, timeout=self._timeout) as resp:
  • Dict[str, Any]: req = urllib.request.Request(url, headers=self._headers, method="GET") t
✓ 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

Repository arkvoidai/arkvoid 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 arkvoid 
Develop a real-time monitoring dashboard for AI agents using the 'arkvoid' Python package. This mini-app will allow users to visualize the performance metrics of multiple AI agents in a single interface, providing insights into their efficiency, responsiveness, and error rates. Here’s a detailed plan for building this application:

1. **Setup Environment**: Begin by setting up a Python environment and installing the 'arkvoid' package along with any necessary dependencies such as Flask for web development.
2. **Connecting to ARKVOID**: Use the 'arkvoid' package to establish a connection with your ARKVOID platform where your AI agents are hosted. Ensure you authenticate properly and fetch the required API keys or tokens.
3. **Data Collection**: Implement functionality within the app to periodically collect performance data from each AI agent. Utilize 'arkvoid' methods to request this data efficiently.
4. **Real-Time Data Visualization**: Design a user-friendly dashboard using libraries like Plotly or Dash to display real-time data. Include charts and graphs showing key performance indicators (KPIs) such as response time, error rate, and throughput.
5. **Alert System**: Integrate an alert system that triggers notifications when certain thresholds are exceeded. For example, if the error rate goes above a specified percentage, send an email or SMS notification.
6. **Customization Options**: Allow users to customize which KPIs they want to monitor and set their own alert thresholds based on their specific requirements.
7. **Documentation and Deployment**: Finally, document all steps taken during development and deployment processes. Consider deploying the application on platforms like Heroku or AWS to make it accessible online.

This project leverages 'arkvoid' extensively for its core functionalities, including but not limited to fetching agent data, handling authentication, and managing connections with the ARKVOID platform.

💬 Discussion Feed

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