aws-exporter

v0.5.2 safe
3.0
Low Risk

(No description)

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe with low risk indicators across multiple categories. The primary concern is the credential risk due to environment variable usage, but this is standard practice and not inherently suspicious.

  • Low network and shell risks
  • No obfuscation detected
  • Potential credential risk from environment variable usage
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code appears to be reading environment variables for configuration purposes, which is common practice and not indicative of malicious activity.
  • Metadata: The maintainer has only one package, indicating potential new or less active account.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. util.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2072 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
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 94 commits in ClarkSource/aws-exporter
  • Active community — 5 or more distinct contributors

🔬 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 score 10.0

Found 4 credential access pattern(s)

  • def main(): delay = int(os.environ.get('AWS_EXPORTER_POLL_DELAY', 30)) port = int(os.environ.get('AW
  • L_DELAY', 30)) port = int(os.environ.get('AWS_EXPORTER_PORT', 8000)) log_level = getattr(logging, os.e
  • log_level = getattr(logging, os.environ.get('AWS_EXPORTER_LOG_LEVEL', 'info').upper()) metrics_config = {
  • additional_ami_owners = os.environ.get('AWS_EXPORTER_EC2_AMI_OWNERS') if additional_ami_owners is n
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: clark.de

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ClarkSource/aws-exporter appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aljosha Friemann" 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 aws-exporter 
Create a monitoring dashboard for your AWS resources using Python and the 'aws-exporter' package. This mini-application will serve as a lightweight solution to monitor various AWS services such as EC2 instances, S3 buckets, and RDS databases. The goal is to collect metrics from these services and visualize them in real-time on a simple web interface. Here’s a detailed breakdown of the steps and features you should implement:

1. **Setup and Installation**: Start by setting up a virtual environment for your project and installing necessary packages including 'aws-exporter', Flask for the web server, and Plotly for data visualization.
2. **Authentication and Configuration**: Configure AWS credentials and setup the 'aws-exporter' package to authenticate with your AWS account. Ensure you have the appropriate permissions to access the resources you want to monitor.
3. **Data Collection**: Use 'aws-exporter' to collect metrics from specific AWS services. For example, gather CPU usage data from EC2 instances, storage usage from S3 buckets, and database performance metrics from RDS instances.
4. **Data Processing**: Process the collected data to make it suitable for visualization. This might involve filtering out irrelevant data points, aggregating data over time periods, or transforming raw data into more meaningful metrics.
5. **Web Interface**: Develop a simple web interface using Flask where users can view the collected and processed metrics. Implement basic navigation and interactive elements to allow users to switch between different types of metrics or filter based on specific criteria.
6. **Visualization**: Integrate Plotly into your Flask application to create dynamic visualizations of the metrics. Design charts and graphs that clearly represent the state of your AWS resources over time.
7. **Testing and Deployment**: Test your application thoroughly to ensure all components work as expected. Once tested, deploy your application to a cloud service provider like AWS or Heroku.

Optional Features:
- Implement user authentication to restrict access to the monitoring dashboard.
- Add alerting capabilities to notify users via email or SMS when certain thresholds are breached.
- Incorporate historical data analysis tools to help users understand trends over longer periods.
- Allow users to customize the dashboard layout and preferences according to their needs.

💬 Discussion Feed

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