AI Analysis
Final verdict: SAFE
The package shows very low risks across multiple dimensions with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to a new maintainer account and a non-HTTPS link, but there are no clear indicators of malicious intent.
- Low risk scores across network, shell, obfuscation, and credential checks.
- Elevated metadata risk due to a new maintainer account and a non-HTTPS link.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution detected, indicating no immediate risk of command injection or unauthorized system access.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and there's a non-HTTPS link, but no clear signs of typosquatting or other severe risks.
Package Quality Overall: Low (3.4/10)
○ 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 (13053 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
8.0
Active multi-contributor project
4 unique contributor(s) across 100 commits in laoshanxi/app-meshSmall but multi-author team (3–4 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
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
score 3.0
Suspicious email domain flags: Very short email domain: qq.com
Very short email domain: qq.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://supervisord.org/
Git Repository History
Repository laoshanxi/app-mesh appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "laoshanxi" 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 appmesh
Create a network traffic visualization tool using the 'appmesh' Python package. This tool will allow users to monitor and visualize network traffic within their AWS App Mesh environment. Your application should provide real-time insights into service-to-service communication, including request/response times, error rates, and throughput. Here are the key steps and features for your project: 1. **Setup**: Install the 'appmesh' package and configure it to connect to your AWS App Mesh environment. Ensure you have the necessary permissions and credentials set up. 2. **Data Collection**: Utilize the 'appmesh' package to collect data on virtual nodes, virtual routers, and mesh policies from your App Mesh environment. Implement a mechanism to continuously poll for new data at regular intervals. 3. **Data Processing**: Process the collected data to calculate metrics such as average request time, total requests, error rates, and throughput for each service. Store these metrics in a local database or cache for quick access and historical analysis. 4. **Visualization**: Develop a user interface using a web framework like Flask or Django to display the processed data in real-time. Include graphs, charts, and tables to represent different aspects of network traffic. 5. **Alerting System**: Integrate an alerting system that sends notifications when certain thresholds are breached, such as high error rates or low throughput. Users should be able to customize these thresholds based on their specific requirements. 6. **User Interface Enhancements**: Add interactive features such as filtering services, setting time ranges, and toggling between different views (e.g., overall vs. individual service). 7. **Documentation**: Write comprehensive documentation detailing how to install, configure, and use the tool. Include examples and best practices for monitoring network traffic effectively. By following these steps, you'll create a powerful tool for anyone working with AWS App Mesh to gain deeper insights into their network traffic patterns and optimize their services accordingly.
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