🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to the use of eval and pickle.loads, which can lead to arbitrary code execution. While there is no direct evidence of malicious intent, the combination of potential code injection vulnerabilities and signs of low maintainer activity raises concerns about its safety.

High obfuscation risk due to eval and pickle.loads usage
Signs of low maintainer activity

Per-check LLM notes

Network: No network calls detected, which is typical for packages without internet-related functionality.
Shell: The use of shell commands might be legitimate if the package involves system-level operations, but it could also indicate potential risks if not properly sanitized or reviewed.
Obfuscation: The code patterns suggest potential malicious obfuscation due to the use of eval and pickle.loads which can be vectors for executing arbitrary code.
Credentials: No clear evidence of credential harvesting detected.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

oc__" setattr(eval(name), "__doc__", eval(temp)) # pylint: disable=eval-used
etattr(eval(name), "__doc__", eval(temp)) # pylint: disable=eval-used all_items.ap
le.HIGHEST_PROTOCOL) s3 = pickle.loads(s2) assert s.value() == s3.value() del s, s3 as

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

assert success out = subprocess.check_output(['nm', '-gjU', 'tmp/' + f[1]]) for line in out.decode().

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: illinois.edu>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://web.archive.org/web/20250730204937/https://www.aiomfac.caltech.edu/

✓ Git Repository History

Repository open-atmos/PyPartMC appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with PyPartMC

Your task is to develop a mini-application that simulates the dispersion of particulate matter (PM) in an urban environment using the PyPartMC library. This application will allow users to input parameters such as PM emission rates from various sources (e.g., vehicles, factories), meteorological conditions (wind speed, direction, temperature, humidity), and urban geometry details (street width, building heights). The application will then use PyPartMC to model the dispersion of PM in the urban area over time and visualize the results in an interactive map.

Step-by-Step Instructions:
1. Begin by setting up your development environment with Python and installing the PyPartMC package.
2. Design a user-friendly GUI where users can input the necessary parameters for PM emissions, meteorology, and urban geometry.
3. Implement the core functionality using PyPartMC to simulate the dispersion of PM based on the user inputs.
4. Integrate a mapping library like Folium or Plotly to visualize the dispersion pattern on an interactive map.
5. Add features such as saving simulation results, loading previous simulations, and comparing multiple scenarios.
6. Ensure your application has clear documentation and instructions for users.

Suggested Features:
- Real-time visualization of PM concentration changes as simulation progresses.
- Ability to add/remove emission sources dynamically during a simulation.
- Comparative analysis tool allowing side-by-side comparison of different scenarios.
- Detailed reports summarizing PM concentrations at specific locations and times.
- Export options for simulation data in common formats like CSV or JSON.

How to Utilize PyPartMC:
- Use PyPartMC to define the physical properties of the particles being emitted.
- Configure the environmental conditions that affect particle dispersion.
- Set up the computational domain representing the urban area.
- Run simulations using PyPartMC's built-in algorithms to model particle behavior.
- Extract and process the simulation results for visualization and analysis.