🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no signs of malicious activities. While there are some concerns regarding network and shell risks, these appear to be legitimate practices for resource fetching and package building.

Low risk scores across all categories.
No evidence of obfuscation or credential harvesting.
Network and shell risks are within acceptable limits for legitimate package operations.

Per-check LLM notes

Network: The network calls are likely for fetching resources or updates, which is not uncommon but should be scrutinized for URLs and data being sent.
Shell: Shell execution is used here for building the package, which is common in C++ extensions for Python, but needs to be reviewed for legitimacy and potential misuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a single package and the email domain is very short, but no other suspicious elements were found.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

rl(url): try: r = requests.get(url) except requests.ConnectionError: raise Easy
""" try: r = requests.get(url) except requests.ConnectionError: raise Easy

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

.mkdir(parents=True) subprocess.run( ["cmake", ext.sourcedir, *cmake_args], cwd=buil
check=True ) subprocess.run( ["cmake", "--build", ".", *build_args], cwd=bui

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: m.fudan.edu.cn

Very short email domain: m.fudan.edu.cn

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository easy-graph/Easy-Graph appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Fudan DataNET Group" 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 Python-EasyGraph

Your task is to create a social network analysis tool using the Python-EasyGraph library. This tool will help users visualize and analyze the connections within a given set of data representing a social network. Here’s a detailed breakdown of the project requirements:

1. **Data Input**: Users should be able to input their data in CSV format. The CSV file should contain two columns: 'User' and 'Connections', where 'Connections' lists all the users connected to the user in question.
2. **Graph Creation**: Utilize the Python-EasyGraph library to create a graph from the input data. Each user should be a node, and each connection between users should be an edge.
3. **Visualization**: Implement a feature to visually represent the graph. Use Python-EasyGraph’s visualization capabilities to display the network graphically. Consider different layouts such as circular, spring, or spectral to explore various visual representations.
4. **Analysis Tools**: Provide several analytical tools to evaluate the network structure. For example, calculate the degree centrality, betweenness centrality, and clustering coefficient for each node. Display these metrics alongside the graph.
5. **Interactive Features**: Allow users to interact with the graph by highlighting specific nodes or edges. When a node is selected, show its immediate connections and relevant metrics.
6. **Export Functionality**: Enable users to export the analyzed graph and its metrics into a PDF report or another format of their choice.
7. **Documentation**: Write comprehensive documentation explaining how to use the tool, including sample datasets and expected outputs.

To utilize the Python-EasyGraph package effectively, focus on leveraging its graph creation and analysis functionalities. Ensure your code is modular and well-documented, making it easy for others to understand and potentially extend your work.