🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity based on the analysis. It has low risks across all categories except for metadata, where the maintainer's single package might suggest caution.

No network calls or shell executions detected
Low obfuscation and credential risk
Maintainer has only one package listed

Per-check LLM notes

Network: No network calls detected, which is normal unless the package is expected to perform network operations.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags were detected.

🔬 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AndiEcker" 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 ae-app-log

Create a simple web-based application using Flask and the 'ae-app-log' package for logging purposes. This application will allow users to submit their feedback or bug reports through a form. Upon submission, the application will log the details of the feedback or bug report into a file using the 'ae-app-log' package. Additionally, implement a feature where administrators can view all submitted feedback or bug reports from a dedicated admin panel within the application.

Step-by-Step Instructions:
1. Set up a new Flask project and install necessary packages including 'ae-app-log'.
2. Design a user-friendly interface for submitting feedback or bug reports. Include fields such as title, description, and severity level.
3. Implement server-side validation to ensure that all required fields are filled out before submission.
4. Use 'ae-app-log' to log each submission into a file with timestamp, user IP address, and other relevant information.
5. Create an admin panel accessible only to authenticated users. This panel should display all logged feedback or bug reports with filtering options based on date or severity.
6. Enhance the application by adding email notifications to the administrator whenever a new feedback or bug report is submitted.
7. Ensure the application is secure by implementing CSRF protection and input sanitization.
8. Deploy the application to a public server for testing and demonstration purposes.

Features:
- User interface for submitting feedback or bug reports.
- Server-side validation for form submissions.
- Logging of each submission using 'ae-app-log', including metadata like timestamps and user IP addresses.
- Admin panel with authentication for viewing all submitted feedback or bug reports.
- Email notification to administrators upon new submissions.
- Security measures such as CSRF protection and input sanitization.