🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of legitimate functionality but raises concerns due to its newness and lack of supporting metadata.

Metadata risk at 5/10 due to newness and lack of supporting documentation.
No direct evidence of malicious intent but requires further scrutiny.

Per-check LLM notes

Network: The presence of network calls suggests the package may be designed to communicate with external services, which is not inherently malicious but should be reviewed for legitimacy.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with low metadata quality and no associated GitHub repository, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

Cache() self.client = httpx.AsyncClient(timeout=self.config.timeout_seconds) async def dispatch

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 3 day(s) ago
Author "Lujain Khalil" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentis-verify

Create a mini-application that serves as a bridge between various agents and a central system, ensuring secure communication through request verification. This application will use the 'agentis-verify' FastAPI middleware package to authenticate and authorize incoming requests from different agents before processing them. The app should have the following functionalities:

1. **User Authentication**: Implement a simple user authentication mechanism where users can log in and out of the application.
2. **Agent Registration**: Allow agents to register themselves with the system by providing necessary credentials. These credentials will be used to verify their identity during subsequent requests.
3. **Request Verification Middleware**: Utilize the 'agentis-verify' package to integrate a middleware component into your FastAPI application. This middleware will intercept all incoming requests, validate the agent's identity against the Agentis identity platform, and ensure the request is authorized before passing it on to the appropriate endpoint.
4. **Secure Data Exchange**: Ensure that all data exchanged between agents and the system is encrypted. Use HTTPS for secure communication.
5. **Logging and Monitoring**: Implement logging to record all successful and failed authentication attempts. Additionally, set up basic monitoring to track the health of the application and the number of verified requests processed.
6. **Documentation**: Provide comprehensive documentation for both developers and end-users. Include API documentation using Swagger UI integrated with FastAPI.
7. **Testing**: Write unit tests and integration tests to ensure the application works as expected under various conditions.

The 'agentis-verify' package plays a crucial role in this project by handling the verification process seamlessly, allowing developers to focus more on the business logic rather than implementing complex security measures from scratch.