🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some network activity that needs further investigation to ensure legitimacy, coupled with a low level of community engagement from its maintainer. These factors raise concerns about potential supply-chain risks.

network GET requests may be fetching unknown data
maintainer has a new or inactive account

Per-check LLM notes

Network: Network GET requests may be legitimate if the package fetches necessary data from a server, but requires verification of the URL's legitimacy and purpose.
Shell: No shell execution patterns detected, which is normal and safe.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account, and the repository lacks community engagement.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: r = requests.get(url, timeout=20) r.raise_for_status()

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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 2.0

1 maintainer concern(s) found

Author "Johnny Tang" 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 adminbounds

Develop a Python-based geospatial analysis tool called 'GeoBoundChecker' that leverages the 'adminbounds' package to analyze and infer administrative boundary relations within geospatial datasets. This tool will enable users to upload a geospatial dataset (such as a shapefile or GeoJSON), select specific administrative boundaries (e.g., countries, states, counties), and perform various analyses on these boundaries. The application should have the following core functionalities:

1. **Boundary Inference**: Automatically identify and classify administrative boundaries present in the uploaded dataset using the 'adminbounds' package.
2. **Boundary Relation Analysis**: Analyze the spatial relationships between different administrative boundaries (e.g., containment, adjacency, overlap).
3. **Visualization Tool**: Provide an interactive map visualization where users can see the boundaries overlaid on top of each other and explore their relationships visually.
4. **Report Generation**: Generate detailed reports summarizing the findings from the boundary analysis, including statistics on the number of contained regions, adjacent regions, etc.
5. **Custom Query Interface**: Allow users to input custom queries to filter and analyze specific subsets of data based on predefined criteria related to administrative boundaries.

To utilize the 'adminbounds' package effectively, integrate its functions for boundary detection and classification into the backend logic of your application. Ensure that the tool is user-friendly and accessible via a web interface built with Flask or Django, making it easy for both technical and non-technical users to interact with the geospatial data.