appengine-python-standard

v2.0.1 safe
3.0
Low Risk

Google App Engine services SDK for Python 3

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on the low risk scores across all categories, with no clear signs of malicious intent or supply-chain attacks.

  • Low network and metadata risks.
  • No evidence of shell execution or credential harvesting.
Per-check LLM notes
  • Network: The observed network call pattern is likely legitimate, as it appears to be making HTTP GET requests, which could be for fetching resources or updates.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 decoding and dynamic imports may indicate obfuscation, but could also be legitimate for various purposes such as handling configuration files or encrypted data.
  • Credentials: No clear patterns indicating credential harvesting were found.
  • Metadata: The package seems legitimate with no typosquatting attempts and minimal flags.

📦 Package Quality Overall: Low (3.8/10)

○ 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 (2992 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 13 unique contributor(s) across 81 commits in GoogleCloudPlatform/appengine-python-standard
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • rl = root + path response = requests.get(url, **kwargs) if response.status_code != requests.codes.o
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • d): try: return base64.b64decode(value) except (TypeError, binascii.Error) as err:
  • elf, options): lexerMod = __import__(options.lexerClass) self.lexerClass = getattr(lexerMod, options.lexerClass)
  • s.lexerClass) parserMod = __import__(options.parserClass) self.parserClass = getattr(parserMod, options.parserCla
  • on']: try: module = __import__(module_name, {}, {}, 'json') if not hasattr(module, 'JSONEncoder'): messag
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://cloud.google.com/blog/products/serverless/support-for-app-engine-services
✓ Git Repository History

Repository GoogleCloudPlatform/appengine-python-standard appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Google LLC" 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 appengine-python-standard 
Develop a simple task management application using Google App Engine's 'appengine-python-standard' package for Python 3. This application will allow users to create, view, update, and delete tasks. Each task will have a title, description, due date, and status (e.g., pending, completed). Additionally, users should be able to categorize their tasks into different projects. The application will store all task data in Google Cloud Datastore. Here’s a detailed breakdown of the steps and features:

1. **Setup Project**: Create a new directory for your project and initialize it as a Python virtual environment. Install the necessary dependencies, including 'appengine-python-standard'.
2. **Model Design**: Define models for Task and Project using the Datastore API provided by 'appengine-python-standard'. Ensure each model includes appropriate fields and relationships.
3. **User Interface**: Develop a basic HTML/CSS/JavaScript frontend that allows users to interact with the backend API. Use Bootstrap for styling.
4. **Backend Development**: Implement CRUD operations for Tasks and Projects. Each operation should handle user input validation and error handling gracefully.
5. **Authentication**: Integrate Google OAuth2 for user authentication. Allow users to sign up, log in, and manage their accounts.
6. **Deployment**: Deploy your application to Google App Engine using the 'gcloud' command-line tool. Ensure you configure your app.yaml file correctly to specify runtime, services, and other settings.
7. **Testing**: Write unit tests for your backend functions and integration tests for your entire stack to ensure reliability.
8. **Documentation**: Provide clear documentation on how to set up, run, and deploy the application locally and on Google App Engine.

This project will demonstrate proficiency in using 'appengine-python-standard', managing data with Google Cloud Datastore, integrating third-party authentication, and deploying applications to Google App Engine.

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