aiolocust

v0.7.17 safe
4.0
Medium Risk

High performance Python-based load testing tool

🤖 AI Analysis

Final verdict: SAFE

The package aiolocust v0.7.17 is assessed as safe with low risks across all categories except metadata, where it scores slightly higher due to the maintainer having only one package and non-HTTPS links.

  • No network or shell execution risks detected.
  • Low risk for obfuscation and credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, and there are non-HTTPS links which may indicate lack of attention to security practices.

📦 Package Quality Overall: Low (4.2/10)

◈ 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 (9293 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 22 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

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

Email domain looks legitimate: redshirt.se>

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://example.com/
  • Non-HTTPS external link: http://example.com
  • Non-HTTPS external link: http://127.0.0.1:8080
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Lars Holmberg" 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 aiolocust 
Create a high-performance stress testing tool using the 'aiolocust' Python package. Your goal is to develop a mini-application that simulates user traffic on a web application or API endpoint to test its performance under various loads. This tool will help developers understand the limits of their applications and identify potential bottlenecks.

### Project Scope:
- **Application Name:** StressTestSimulator
- **Primary Functionality:** Simulate concurrent users accessing a web application or API endpoint.
- **Target Audience:** Developers who need to perform load testing on their web applications or APIs.

### Core Features:
1. **User Configuration:** Allow users to configure the number of simulated users, the duration of the test, and the URL of the target application/API.
2. **Custom Scripts:** Users should be able to write custom scripts that define the behavior of each simulated user (e.g., login, browse pages, submit forms).
3. **Real-time Monitoring:** Display real-time metrics such as response times, request rates, and error rates during the test.
4. **Report Generation:** After the test, generate a comprehensive report that includes statistics like average response time, maximum response time, total requests made, and any errors encountered.
5. **Scalability:** Ensure the tool can handle a large number of concurrent users efficiently.

### Utilizing 'aiolocust':
- Use 'aiolocust' to handle the asynchronous execution of tasks, which is crucial for simulating many concurrent users without blocking.
- Implement custom tasks within 'aiolocust' tasks to simulate user interactions with the target application.
- Leverage 'aiolocust's built-in support for real-time reporting and data collection to monitor the performance of the target application during the test.
- Explore advanced features of 'aiolocust' to enhance the functionality of your stress testing tool, such as integrating with third-party services for additional analytics or using distributed setups to scale up testing capacity.

### Implementation Steps:
1. Set up a Python environment with 'aiolocust' installed.
2. Design the user interface for configuring tests, including options for setting up the number of users, test duration, and target URL.
3. Develop a script generator that allows users to write custom interaction scripts.
4. Integrate 'aiolocust' into your application to run these scripts asynchronously.
5. Implement real-time monitoring and reporting features based on the collected data from 'aiolocust'.
6. Test your application thoroughly with different configurations and scenarios to ensure reliability and accuracy.
7. Document your project, including setup instructions, usage guidelines, and example configurations.
8. Optionally, consider packaging your application for easy distribution and installation.