TransitionListener

v2.0.1 suspicious
5.0
Medium Risk

Framework for analyzing cosmological first-order phase transitions and their gravitational wave signatures.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate level of risk due to its use of eval(), which can indicate code obfuscation or injection, and the shell execution which might suggest system manipulation. The maintainer's account details are incomplete, raising concerns about the package's origin and reliability.

  • Unusual use of eval()
  • Detection of shell execution
  • Incomplete maintainer details
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Detection of shell execution suggests the package may perform system operations, which could be legitimate but also warrants further investigation to ensure it does not execute unauthorized commands.
  • Obfuscation: The code shows unusual use of eval() which can be a sign of obfuscation or code injection, indicating potential risk.
  • Credentials: No clear signs of credential harvesting detected.
  • Metadata: The maintainer has a new or inactive account with incomplete author details, suggesting potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ror): value = eval(mp[key]["value"]) min_val = mp[key].get("min",
  • ValueError: low = eval(prior[0]) try: high = float(prior[1])
  • alueError: high = eval(prior[1]) if state.config.scan_params[name]["scale"
  • ValueError: low = eval(prior[0]) try: high = float(prior[1])
  • alueError: high = eval(prior[1]) if conf.scan_params[name]["scale"] == "lo
  • er() try: __import__(name) _result(f"import {name}", True, f"{(time.perf_c
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: proc = subprocess.run( cmd, cwd=tmpdir,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: kit.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository tasicarl/TransitionListener appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 TransitionListener 
Develop a mini-application named 'CosmoWaveAnalyzer' using the Python package 'TransitionListener'. This application will serve as a tool for researchers and students interested in cosmology to analyze the gravitational wave signatures associated with cosmological first-order phase transitions. Here's a step-by-step guide on what your application should achieve:

1. **Setup and Installation**: Begin by setting up a virtual environment for Python 3.x. Ensure 'TransitionListener' is installed within this environment.
2. **Data Input Module**: Create a module that allows users to input cosmological data related to first-order phase transitions. This could include parameters like transition temperature, energy density, and other relevant physical constants.
3. **Analysis Engine**: Utilize 'TransitionListener' to process the input data and simulate the gravitational wave signatures resulting from these phase transitions. Implement functionalities provided by 'TransitionListener' such as frequency analysis, amplitude calculation, and signal processing techniques specific to cosmological events.
4. **Visualization Tool**: Develop a visualization component that graphically represents the analyzed data. Users should be able to see plots of gravitational wave signals over time, frequency spectra, and other relevant visualizations.
5. **Report Generation**: Integrate a feature that generates comprehensive reports summarizing the analysis results. These reports should include key findings, graphs, and any relevant scientific interpretations based on the analysis.
6. **User Interface**: Design a simple yet intuitive user interface using a framework like PyQt or Tkinter. The UI should facilitate easy interaction with all the modules of 'CosmoWaveAnalyzer', making it accessible even to those without extensive programming knowledge.
7. **Documentation**: Provide detailed documentation explaining how to use each feature of 'CosmoWaveAnalyzer', including installation instructions, usage examples, and a glossary of terms related to cosmology and gravitational waves.

Suggested Features:
- Support for multiple types of cosmological models and phase transition scenarios.
- Advanced filtering options for gravitational wave signals.
- Integration with external databases or APIs for real-time data updates.
- Export functionality for analysis results in various formats (CSV, PDF, etc.).

Ensure that 'TransitionListener' is utilized throughout the development process, particularly in the core analysis engine where its specialized functions for cosmological data are crucial.