akernel-runtime

v0.1.25 suspicious
5.0
Medium Risk

Agent Kernel: a CLI-first context-native agent runtime prototype.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to the execution of shell commands and network calls, which could potentially be exploited for malicious activities. However, there is no evidence of direct malicious behavior or credential harvesting.

  • Execution of shell commands
  • Network calls to external URLs
Per-check LLM notes
  • Network: Network calls to external URLs are observed, which could be for legitimate purposes like fetching remote resources, but require further investigation into the base_url destination.
  • Shell: Execution of shell commands within the package is concerning and could indicate potential malicious behavior, especially if the commands are not clearly documented or serve an unexpected purpose.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, suggesting it might be a new or less active account.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_runtime.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (15479 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 420 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 66 commits in huanxin0825-ctrl/context-akernel
  • Single author but highly active (66 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • de("utf-8") request = urllib.request.Request( self.base_url + path, data=
  • [str, Any]: request = urllib.request.Request( self.base_url + path, heade
  • def _open_json(self, request: urllib.request.Request) -> dict[str, Any]: last_error: urllib.error
  • try: with urllib.request.urlopen(request, timeout=self.timeout_seconds) as response:
  • sponse() with patch("urllib.request.urlopen", side_effect=fake_urlopen): response =
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • cwd).resolve()) process = subprocess.Popen( split_command(command), cwd=cwd, st
  • try: completed = subprocess.run( args, cwd=self.workspace.ro
✓ 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 huanxin0825-ctrl/context-akernel appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Context Kernel 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 akernel-runtime 
Create a command-line interface (CLI) utility named 'AgentPrompter' using the 'akernel-runtime' package. This utility will serve as a simple yet powerful tool for managing and executing agent-based tasks within a user's local environment. The goal of 'AgentPrompter' is to streamline the process of deploying, monitoring, and controlling AI agents for various tasks such as data processing, automation, and more.

### Key Features:
1. **Agent Deployment**: Users should be able to define and deploy custom agents through a simple configuration file or directly from the CLI. Each agent should have its own unique set of instructions and capabilities.
2. **Task Execution**: Once deployed, users can assign tasks to these agents. Tasks can range from simple commands like fetching data from a URL to more complex operations involving machine learning model inference.
3. **Status Monitoring**: Provide real-time status updates on the execution of tasks assigned to agents. Users should be able to see if their tasks are pending, running, completed, or failed.
4. **Interactive Mode**: Implement an interactive mode where users can communicate directly with the agents in real-time, allowing them to provide additional instructions or inputs during task execution.
5. **Logging and Reporting**: Ensure all interactions and outputs from the agents are logged for auditing and reporting purposes. Users should be able to generate detailed reports on agent performance and task outcomes.
6. **Security Measures**: Incorporate basic security measures to protect against unauthorized access and ensure that sensitive data handled by agents remains secure.

### Utilizing 'akernel-runtime':
- Use 'akernel-runtime' to manage the lifecycle of your agents, including initialization, execution, and termination.
- Leverage the context-native capabilities of 'akernel-runtime' to allow agents to interact seamlessly with the local environment, accessing necessary resources and services.
- Employ 'akernel-runtime' for handling the communication between the CLI and the agents, ensuring smooth interaction and efficient task execution.

### Development Steps:
1. **Setup Project Environment**: Initialize a new Python project and install the required packages, including 'akernel-runtime'.
2. **Design Configuration File**: Create a configuration file format that allows users to specify agent details and tasks easily.
3. **Implement CLI Commands**: Develop CLI commands for deploying agents, assigning tasks, and monitoring their status.
4. **Develop Core Logic**: Write the core logic for initializing agents, executing tasks, and managing their interactions with the local environment using 'akernel-runtime'.
5. **Enhance User Interaction**: Add support for interactive mode and implement logging and reporting functionalities.
6. **Test Thoroughly**: Conduct thorough testing to ensure all features work as expected and address any security concerns.
7. **Document and Release**: Prepare comprehensive documentation and release the utility for public use.

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