LoadStrike

v1.0.23601 suspicious
8.0
High Risk

Python SDK for in-process load execution, traffic correlation, and reporting.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to high shell execution and network activity, with no clear justification for these behaviors. The low metadata score adds to the suspicion.

  • High shell risk
  • Unjustified network calls
  • Lack of maintainers' credibility
Per-check LLM notes
  • Network: The network calls suggest the package is making external requests which may not be necessary for its stated purpose, indicating potential data exfiltration.
  • Shell: The shell execution patterns involve running system commands that could modify system configurations or access sensitive information, suggesting high risk of malicious intent.
  • Metadata: The maintainer has only one package and lacks a GitHub repository, which could indicate a less established or potentially suspicious presence.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: replay_request = urllib.request.Request( url, data=data,
  • ethod, ) with urllib.request.urlopen(replay_request, timeout=30) as response:
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • - remainder)) return base64.b64decode(normalized.encode("ascii")) @staticmethod def _norm
  • y: return base64.b64decode(text.encode("ascii")).decode("utf-8") except
  • try: body = base64.b64decode(body_base64) except (ValueError, TypeError):
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: result = subprocess.run( ["/usr/sbin/ioreg", "-rd1", "-c", "IOPlatfo
  • try: result = subprocess.run( ["cmd.exe", "/c", "reg", "query", r"HKLM\SO
✓ 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 2.0

1 maintainer concern(s) found

  • Author "LoadStrike" 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 LoadStrike 
Your task is to develop a robust, real-time load testing tool for web applications using the Python package 'LoadStrike'. This tool will allow users to simulate various levels of user traffic on their web applications to test performance under different loads. Your goal is to create a fully functional mini-application that not only tests but also correlates and reports traffic data effectively.

**Step-by-Step Development Guide:**
1. **Setup Environment**: Begin by setting up your development environment with Python and installing the LoadStrike package.
2. **Define Test Scenarios**: Create multiple test scenarios that users can select from, each simulating different types of traffic patterns such as high concurrency requests, steady-state requests, and spike tests.
3. **Implement Traffic Simulation**: Use LoadStrike to implement traffic simulation based on the selected test scenarios. Ensure you can configure parameters like number of concurrent users, request rate, and duration of the test.
4. **Traffic Correlation**: Utilize LoadStrike's capabilities to correlate traffic data from different sources, such as front-end requests and back-end responses, to understand the complete picture of how traffic affects the application.
5. **Reporting and Visualization**: Develop a reporting module that generates comprehensive reports and visualizations post-test execution. These should include metrics like response times, throughput, error rates, and resource utilization.
6. **User Interface**: Optionally, build a simple command-line interface or a web-based UI for users to interact with your tool easily. This UI should allow users to select test scenarios, start tests, and view results.
7. **Documentation and Testing**: Write clear documentation explaining how to use the tool and its features. Also, ensure thorough testing of all functionalities to guarantee reliability and accuracy.

**Suggested Features:**
- Support for multiple protocols (HTTP/HTTPS, gRPC)
- Customizable test scenarios with adjustable parameters
- Real-time monitoring during tests
- Historical data storage for future analysis
- Integration with popular monitoring tools (e.g., Prometheus, Grafana)

**How to Utilize LoadStrike:**
LoadStrike provides essential functions for generating load, correlating traffic, and reporting results. Use its API to define load patterns, execute tests, and analyze outcomes. For example, you might use `loadstrike.load_generator.generate_load()` to simulate traffic, `loadstrike.correlator.correlate_data()` to connect front-end and back-end data, and `loadstrike.reporter.create_report()` to compile test results into readable formats.