🤖 AI Analysis

Final verdict: SAFE

The package appears to function as intended for an HTTP client, with minimal risks identified across various checks. The metadata score is slightly elevated due to potential authorship concerns, but overall, there's no strong evidence of malicious intent or supply-chain attack.

Low risk scores across network, shell, obfuscation, and credential checks.
Elevated metadata risk due to potential authorship issues.

Per-check LLM notes

Network: The observed network patterns are typical for an HTTP client that manages sessions and performs POST requests, suggesting normal functionality rather than malicious activity.
Shell: No shell execution patterns were detected, indicating no risk associated with shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No secret harvesting patterns detected, suggesting safe handling of credentials.
Metadata: The package shows low authorship effort and could potentially be suspicious due to the short email domain.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

:-1] self._session = requests.Session() self._handle_auth_method() self._session
" try: response = requests.post(endpoint, auth=auth, data=data, timeout=timeout, verify=ssl_
son" latest_version = requests.get(pypi_url, timeout=3).json()["info"]["version"] if c

✓ 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 score 3.0

Suspicious email domain flags: Very short email domain: em.ag>

Very short email domain: em.ag>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository fluid40/aas-http-client appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aas-http-client

Create a Python-based mini-application named 'AASDataCollector' that leverages the 'aas-http-client' package to interact with Asset Administration Shell (AAS) servers. This application will serve as a data collector and analyzer, capable of fetching, processing, and presenting data from multiple AAS servers in real-time. The primary goal is to demonstrate the flexibility and power of the 'aas-http-client' package in handling complex HTTP requests and responses.

**Application Requirements:**
- **Data Collection:** The application must be able to fetch data from multiple predefined AAS servers. Users should be able to add new server URLs through a configuration file.
- **Real-Time Data Processing:** Implement basic data processing capabilities such as filtering, sorting, and aggregating data based on specific criteria (e.g., time range, sensor type).
- **Visualization:** Integrate a simple graphical user interface (GUI) using Tkinter or a similar library to display collected and processed data in real-time.
- **Error Handling:** Ensure robust error handling to manage issues like network failures, invalid responses, and timeouts gracefully.
- **Configuration Management:** Use a configuration file (JSON format) to store server URLs, API keys, and other settings.
- **Logging:** Implement logging to record all activities including successful data fetches, errors, and user actions.

**Utilizing 'aas-http-client':**
- Use the 'aas-http-client' package to establish connections with AAS servers, send HTTP GET requests to retrieve data, and handle the responses efficiently.
- Leverage the package's features to manage different authentication methods, headers, and query parameters required by various AAS servers.
- Explore advanced functionalities such as retries on failure, timeout management, and asynchronous requests to enhance reliability and performance.

**Steps to Build the Application:**
1. Set up a Python virtual environment and install necessary packages including 'aas-http-client', 'tkinter', and 'json'.
2. Design the configuration file structure to include server URLs, API keys, and other configurable settings.
3. Develop the core data collection module using 'aas-http-client' to fetch data from AAS servers.
4. Implement the data processing logic to filter, sort, and aggregate data according to user-defined criteria.
5. Create a GUI using Tkinter to visualize the fetched and processed data in real-time.
6. Add comprehensive error handling and logging mechanisms to ensure the application runs smoothly and provides useful diagnostics.
7. Test the application thoroughly with mock data and real AAS servers to validate its functionality and performance.
8. Document the application setup, usage, and customization options for end-users.

This project aims to showcase the versatility of the 'aas-http-client' package while providing a practical tool for AAS data analysis.