autonet-computer

v0.2.9 suspicious
4.0
Medium Risk

Decentralized AI alignment network with agent orchestration framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risk factors but raises concerns due to its alpha release status and the author's limited history with PyPI.

  • Alpha pre-release with potential breaking changes.
  • Author has only one package on PyPI.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet connectivity.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: The author has only one package, which might indicate a new or less active account, raising some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (15940 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in autonet-code/node
  • Single author but highly active (100 commits)

🔬 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

Repository autonet-code/node appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ATN 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 autonet-computer 
Create a decentralized AI alignment application named 'AIAlignNet' using the Python package 'autonet-computer'. This application will serve as a proof-of-concept for managing and aligning multiple AI agents within a decentralized network, ensuring they work towards common goals without centralized control. The app should include the following features:

1. **Agent Registration**: Allow users to register new AI agents within the network. Each agent should have unique identifiers and roles.
2. **Task Assignment**: Implement a system where tasks can be assigned to agents based on their capabilities and current workload. Tasks could range from simple data processing to complex problem-solving.
3. **Decentralized Communication**: Use 'autonet-computer' to facilitate communication between agents in a decentralized manner, ensuring no single point of failure.
4. **Conflict Resolution Mechanism**: Develop a mechanism to resolve conflicts when agents disagree on task outcomes or methods. This could involve voting systems or consensus algorithms.
5. **Monitoring & Reporting**: Provide real-time monitoring and reporting tools to track the performance of each agent and the overall network efficiency.
6. **User Interface**: Design a user-friendly interface for users to interact with the network, including adding/removing agents, assigning tasks, and viewing reports.

The 'autonet-computer' package will be utilized to manage the decentralized network structure, handle agent communications, and ensure the alignment of AI behaviors across the network. Focus on demonstrating how decentralization enhances network resilience and scalability while maintaining coherent goal alignment among diverse AI entities.

💬 Discussion Feed

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