AI Analysis
Final verdict: SAFE
The package shows low risk across multiple categories, with no immediate signs of malicious intent or risky behavior. The metadata suggests a new or less active maintainer, but this alone does not indicate a supply-chain attack.
- Low network, shell, obfuscation, and credential risks
- Missing maintainer's author name and low package activity
Per-check LLM notes
- Network: The use of requests.Session with retries is common for handling network requests and improving reliability, but it could potentially be used for data exfiltration if misused.
- Shell: No shell execution patterns detected, which is normal and indicates no immediate risk of executing system commands from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author name is missing or very short and the maintainer has only one package on PyPI, indicating potential low activity or newness.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
ts.Session: session = requests.Session() retry = Retry( total=_RETRY_TOTAL,
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: protonmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository gadzhi/pyiiko appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 Pyiiko
Create a restaurant management mini-app using the Pyiiko Python package, which interfaces with the iiko ERP system. This app will allow restaurant managers to perform various tasks such as managing orders, tracking inventory, and generating sales reports directly from their smartphones or tablets. 1. **Project Setup**: Begin by setting up a new Python project and installing Pyiiko along with any other necessary libraries such as Flask for web framework or Streamlit for a more user-friendly interface. 2. **Authentication Module**: Implement an authentication module that allows users to log in using their iiko ERP credentials. Utilize Pyiiko's authentication functions to validate these credentials and establish a secure session. 3. **Order Management Interface**: Develop a feature within the app that enables users to view, modify, and cancel orders. Use Pyiiko's order management functions to fetch, update, and delete orders as required. 4. **Inventory Tracking**: Integrate an inventory tracking feature that updates in real-time based on order status changes. Use Pyiiko to pull data on current stock levels and notify managers when items are running low. 5. **Sales Reporting Tool**: Build a tool that generates daily, weekly, and monthly sales reports. Use Pyiiko to retrieve relevant transaction data and process it to generate insightful reports. 6. **User-Friendly Interface**: Ensure the app has a clean, intuitive UI/UX design suitable for non-technical users. Consider using Streamlit for its simplicity in building interactive web apps. 7. **Testing & Deployment**: Thoroughly test the app to ensure all features work correctly and securely. Once satisfied, deploy the app to a cloud platform like Heroku or AWS. In summary, this mini-app leverages Pyiiko’s capabilities to provide a robust set of tools for restaurant managers, streamlining their daily operations and improving overall efficiency.