JSONL-LOGGER

v80.80.80 suspicious
4.0
Medium Risk

Async queue-based structured JSONL logging with thread-safe performance and auto-detected module names

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its recent creation and lack of maintainer history, along with a detected shell execution that warrants further investigation.

  • Metadata risk indicates a new and possibly abandoned package.
  • Shell risk suggests potential runtime checks or malicious activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a logging library.
  • Shell: Detection of shell execution may indicate the package is performing some form of runtime check or self-test, but could also be a sign of potential malicious activity. Further investigation is needed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows signs of being new and potentially abandoned, with a missing maintainer history which raises suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • st emit) result = subprocess.run( ["python3", "-c", "import JSONL_LOGGER as m
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: null.net>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 JSONL-LOGGER 
Create a small utility application in Python that leverages the JSONL-LOGGER package to log events from a simulated real-time data stream. This application will simulate a simple stock market data feed where it receives continuous updates about stock prices and logs them in a structured JSON Lines format.

Steps:
1. Set up your development environment with Python installed.
2. Install the JSONL-LOGGER package using pip.
3. Create a class named `StockMarketLogger` which initializes the JSONL-LOGGER instance with a specified log file path.
4. Implement a method within `StockMarketLogger` to simulate receiving stock price updates. Each update should be a dictionary containing at least 'symbol', 'price', and 'timestamp' fields.
5. Use JSONL-LOGGER to log each received stock price update as a separate JSON line in the log file.
6. Ensure that the logging process is asynchronous and thread-safe to handle multiple incoming updates efficiently.
7. Add functionality to the `StockMarketLogger` class to periodically flush the log buffer to disk, ensuring no data loss in case of application termination.
8. Write a main function that creates an instance of `StockMarketLogger`, starts simulating stock price updates, and runs for a configurable duration.
9. Test the application by running it and verifying that the log file contains the expected structured JSON Lines entries.

Suggested Features:
- Implement a command-line interface to configure the log file path and simulation duration.
- Include error handling to manage potential issues during logging, such as disk full errors or permission issues.
- Provide an option to enable or disable periodic flushing based on user preference.
- Extend the simulated stock price updates to include additional information like volume and change percentage.

How JSONL-LOGGER is Utilized:
- JSONL-LOGGER is used to asynchronously log each stock price update as a structured JSON object. It ensures thread safety, making it suitable for applications that receive multiple concurrent updates.
- The auto-detected module names feature can be utilized to automatically capture the source of each log entry, aiding in debugging and analysis.
- The package's ability to handle large volumes of log data efficiently makes it ideal for applications dealing with real-time data streams.