🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package does not appear to have direct malicious intent or engage in risky network activities, its complex import patterns suggest potential obfuscation. Additionally, the unknown author and low repository activity raise concerns about its provenance and maintenance.

Complex import patterns indicating possible obfuscation
Unknown author and low repository activity

Per-check LLM notes

Network: No network calls detected, which is typical for benign packages.
Shell: Use of shell execution for git operations seems to be related to version control rather than malicious intent.
Obfuscation: The code appears to be using complex import patterns which may indicate an attempt to obscure the true functionality of the package.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The package shows some red flags such as an unknown author and low activity in the git repository, but there's no clear evidence of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

[str, str] = {} package = __import__(module_name, fromlist=[""]) for _, name, _ in pkgutil.walk_packages( packag
+ "." ): module = __import__(name, fromlist=[""]) for name, cls in inspect.getmembers(module, inspect

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

bprocess. """ return subprocess.run(cmd, check=check, text=text, **kwargs) def git_clean() ->
return ( subprocess.check_output(["git", "rev-parse", "HEAD"]) .decode("utf-8")

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: lpsc.in2p3.fr>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 EEmiLib

Develop a mini-application that predicts electron emission rates based on experimental conditions using the EEmiLib Python package. This application will serve as a tool for physicists and materials scientists to model electron emission from surfaces under various environmental conditions. The application should include the following functionalities:

1. User Input Interface: Design a simple GUI where users can input experimental parameters such as temperature, electric field strength, and surface material properties.
2. Model Selection: Implement a feature within the app that allows users to choose between different electron emission models supported by EEmiLib, such as Fowler-Nordheim, Child-Langmuir, or any other relevant models provided by the library.
3. Data Visualization: Integrate a plotting module to visualize the predicted electron emission rate over time or under varying conditions, helping users understand the impact of each parameter on electron emission.
4. Save & Load Sessions: Enable users to save their session configurations including input parameters and selected models, and allow them to load these sessions later for further analysis or modification.
5. Documentation & Help: Provide comprehensive documentation and a help section within the app explaining how to use each feature, what the output means, and any assumptions made by the models.

The core of your application will heavily rely on the EEmiLib package, which you'll use to fit experimental data and predict electron emission rates based on the user inputs. Ensure that your implementation showcases the versatility and power of EEmiLib while making it accessible and understandable for non-expert users.