🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential areas but has a moderate metadata risk due to its newness and limited maintenance. This warrants further investigation to rule out any potential supply-chain attacks.

moderate metadata risk
potential supply-chain attack risk

Per-check LLM notes

Network: The presence of network calls with an incomplete header update might indicate misconfiguration but does not necessarily imply malicious intent without further context.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The package appears to be newly created with limited activity and a single maintainer, which could indicate potential risk.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1678 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

4 type-annotated function signatures (partial)

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

.app" self.session = requests.Session() self.session.headers.update({ "Autho

✓ 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: agentsafe.app

✓ 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

Only one version has ever been released — brand new package
Author "AegisLayer" 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 agentsafe-sdk

Create a web-based dashboard application using Flask in Python that integrates with the 'agentsafe-sdk' package to provide real-time URL trust scoring for URLs entered by users. The application should allow users to input any URL and receive a score indicating the level of trustworthiness of the site. Additionally, the app should display a brief summary of the analysis performed to generate the score, including key factors such as domain reputation, presence of malicious content, and other relevant metrics.

Key Features:
1. User Interface: Develop a clean, user-friendly interface where users can enter URLs and view the results of the trust scoring.
2. Real-Time Scoring: Utilize the 'agentsafe-sdk' package to fetch real-time data about the entered URL and calculate its trust score.
3. Detailed Analysis Report: Provide a breakdown of the factors considered in calculating the trust score, highlighting potential risks or warnings.
4. Visualization: Implement charts or graphs to visually represent the trust score and the contributing factors.
5. Historical Data: Allow users to save their queries and view historical data on previously analyzed URLs.
6. Security Measures: Ensure all user inputs are sanitized to prevent security vulnerabilities and protect against common attacks.

Steps to Build the Application:
1. Set up a Flask environment and install necessary packages, including 'agentsafe-sdk'.
2. Design the front-end layout using HTML/CSS and integrate it with Flask.
3. Implement the backend logic to handle URL input, call the 'agentsafe-sdk' API, and process the returned data.
4. Create functions to generate detailed analysis reports based on the received data from the 'agentsafe-sdk'.
5. Develop visualization tools like pie charts or bar graphs to display the trust score components.
6. Add functionality to store and retrieve historical data for users.
7. Test the application thoroughly, focusing on security aspects and usability.