🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential misuse due to missing maintainer information and lack of an associated GitHub repository, raising concerns about its origin and maintenance.

Lack of maintainer information
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The package has some red flags such as lack of maintainer information and no associated GitHub repository, but there's not enough evidence to conclusively determine 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

Email domain looks legitimate: deepfinance.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 adapter-engine-execution

Create a mini-application named 'AccountManager' that leverages the Python package 'adapter-engine-execution' to manage user accounts within a fictional online service platform. This application will serve as a simplified version of an account management system, focusing on user registration, login, and basic profile management functionalities.

Step 1: Set up the Project Environment
- Initialize a new Python virtual environment and install the 'adapter-engine-execution' package along with other necessary libraries such as Flask for web framework and SQLAlchemy for ORM support.

Step 2: Define User Model
- Use SQLAlchemy to define a User model that includes fields like username, email, password hash, and profile information such as first name, last name, and date of birth.

Step 3: Implement Registration and Login
- Create endpoints for user registration and login using Flask. For registration, ensure that passwords are hashed before storing them in the database. For login, validate credentials against the stored user data and return a session token upon successful authentication.

Step 4: Integrate Adapter Engine Execution
- Utilize 'adapter-engine-execution' to handle account-related operations dynamically. This could involve executing different adapters based on the type of account action being performed (e.g., social media account linking, email verification).

Step 5: Profile Management
- Develop functionality allowing users to update their profiles once logged in. Ensure that sensitive information like passwords require re-authentication before updates.

Suggested Features:
- Social Media Account Linking: Allow users to link their existing social media accounts for easier sign-in or additional security.
- Email Verification: Send verification emails when a new account is registered or when updating email addresses.
- Password Recovery: Implement a secure password recovery process using temporary tokens sent via email.

The 'adapter-engine-execution' package plays a crucial role in making the AccountManager flexible and adaptable. It allows the app to easily integrate various third-party services for account management tasks, enhancing its functionality and user experience without requiring significant changes to the core codebase.