🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious activity such as network calls, shell executions, or credential harvesting. However, the incomplete author information and apparent newness or inactivity of the maintainer's account raise concerns about its provenance.

Incomplete maintainer information
New or inactive maintainer account

Per-check LLM notes

Network: No network calls detected, which is normal for a package focused on local data storage and processing.
Shell: No shell execution patterns detected, consistent with a benign package designed for graph database operations.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The maintainer's author information is incomplete and the account seems new or inactive, which raises some concern but not enough to definitively label it as malicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

Repository aevum-labs/aevum 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 aevum-store-oxigraph

Create a mini-application called 'Semantic Knowledge Explorer' that leverages the 'aevum-store-oxigraph' package to store and query semantic data. This application will allow users to input triples (subject-predicate-object) which represent relationships in a knowledge graph, and then perform various queries to retrieve information based on these relationships.

Steps to build the application:
1. Setup the project environment with Python and install the 'aevum-store-oxigraph' package.
2. Design a simple user interface where users can input triples. Each triple should consist of a subject, predicate, and object.
3. Implement functionality to add these triples to the knowledge graph using the 'aevum-store-oxigraph' package.
4. Create a feature to allow users to query the knowledge graph. Queries should be able to filter based on subjects, predicates, objects, or combinations thereof.
5. Implement a visualization component that shows the connections between different entities in the form of a graph or network diagram.
6. Add error handling to manage incorrect inputs and database operations.
7. Test the application thoroughly to ensure all functionalities work as expected.
8. Deploy the application locally or on a web server for easy access.

Suggested Features:
- Support for multiple types of predicates to enrich the knowledge graph.
- User authentication to differentiate between public and private graphs.
- A history log of recent queries for each user.
- Exporting the knowledge graph data in a standard format like RDF.

Utilizing 'aevum-store-oxigraph':
This package serves as the backend storage for the knowledge graph, providing a robust and efficient way to handle the data. It integrates seamlessly with the application to enable adding, querying, and managing semantic data efficiently. Users can interact with the application through a user-friendly interface while the 'aevum-store-oxigraph' package handles the underlying complexities of storing and retrieving data.