ankor

v0.5.160 suspicious
7.0
High Risk

Standalone workflow automation and monitoring for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to dynamic code execution and lacks essential metadata like author details and a GitHub repository, raising concerns about its provenance and purpose.

  • High obfuscation risk due to use of 'exec'
  • Missing author information and GitHub repository
Per-check LLM notes
  • Network: The network calls appear to be internal or localhost requests, which might be part of the package's functionality but could warrant further investigation.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of dynamic code execution via 'exec' indicates potential obfuscation or evasion techniques, raising suspicion.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The package shows some red flags such as missing author information and a lack of a GitHub repository, but there's no clear evidence of malicious intent or typosquatting.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1349 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

  • 211 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • } async with httpx.AsyncClient(timeout=_ai_request_timeout()) as client: respon
  • } async with httpx.AsyncClient(timeout=_ai_request_timeout()) as client: async
  • ponse: async with httpx.AsyncClient() as client: url = f"http://localhost:{vite_
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • c_source(source_code) exec(compile(tree, f"<ankor-node:{name}>", "exec"), namespace)
  • c_source(source_code) exec(compile(tree, f"<ankor-workflow:{name}>", "exec"), namespace)
  • rce(source_code) exec(compile(tree, f"<ankor-node:{name}>", "exec"), namespace) raw_fn = namespace.get("run") or nam
  • rce(source_code) exec(compile(tree, f"<ankor-workflow:{name}>", "exec"), namespace) raw_fn = namespace.get("run") or nam
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 ankor 
Your task is to develop a small but functional utility using the Python package 'ankor'. This utility will automate the process of monitoring and managing a simple web server's uptime status and performance metrics. Here’s a detailed breakdown of the project requirements and steps:

1. **Project Overview**: Create a utility named 'WebMonitor' which periodically checks the uptime status and performance metrics (e.g., response time, server load) of a specified web server.
2. **Core Features**:
   - **Uptime Monitoring**: Continuously monitor if the web server is up and running.
   - **Performance Metrics**: Collect and store basic performance metrics like response time and server load.
   - **Alerting Mechanism**: Send alerts via email or SMS when the server is down or when certain thresholds (e.g., response time > 500ms) are breached.
3. **Using Ankor**:
   - **Workflow Automation**: Use 'ankor' to define workflows that handle the monitoring tasks, including periodic checks and alerting processes.
   - **Monitoring and Logging**: Leverage 'ankor's monitoring capabilities to log the status and performance data efficiently.
4. **Development Steps**:
   - Step 1: Set up your development environment with Python and install 'ankor'.
   - Step 2: Define a workflow in 'ankor' to perform periodic HTTP requests to the target web server.
   - Step 3: Implement logic to parse the response and extract performance metrics.
   - Step 4: Integrate 'ankor' to schedule these checks at regular intervals.
   - Step 5: Implement an alerting system using an external service (e.g., SMTP for emails).
   - Step 6: Utilize 'ankor's logging capabilities to keep track of all monitoring activities and events.
5. **Additional Enhancements** (Optional):
   - **Dashboard**: Develop a simple dashboard using Flask or a similar framework to visualize the collected data.
   - **Database Integration**: Store the monitoring data in a database (e.g., SQLite, PostgreSQL) for historical analysis.

This project aims to showcase 'ankor's capabilities in workflow automation and monitoring within a practical context.

💬 Discussion Feed

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