AI Analysis
The package shows moderate risk due to potential code obfuscation practices, though it lacks clear indicators of malicious intent such as shell execution or credential theft.
- High obfuscation risk
- No secure links or GitHub repository
Per-check LLM notes
- Network: The network patterns detected are typical for handling HTTP requests in a way that supports both Python 2 and 3, suggesting legitimate functionality rather than malicious activity.
- Shell: No shell execution patterns were detected, indicating low risk of direct command execution from the package.
- Obfuscation: The presence of base64 and zlib operations suggests possible obfuscation to hide code logic.
- Credentials: No direct evidence of credential harvesting patterns was found.
- Metadata: The package has a non-secure external link and no GitHub repository, but the maintainer seems legitimate with only one package listed.
Package Quality Overall: Low (4.8/10)
Test suite present β 12 test file(s) found
12 test file(s) detected (e.g. __init__.py)
Some documentation present
Brief PyPI description (655 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
11 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β contributor count unavailable
Heuristic Checks
Found 5 network call pattern(s)
if PYTHON_3: Request = urllib.request.Request else: Request = urllib2.Request if PYTHON_3PYTHON_3: HTTPHandler = urllib.request.HTTPHandler else: HTTPHandler = urllib2.HTTPHandlerif PYTHON_3: return urllib.request.urlopen(*args, **kwargs) else: return urllib2.if PYTHON_3: return urllib.request.build_opener(*args, **kwargs) else: return url) _requests_session = requests.Session() adapter = requests.adapters.HTTPAdapter(
Found 6 obfuscation pattern(s)
alue = parts value = base64.b64decode(value) value = legacy.str(value) parts = vreturn None data = base64.b64decode(data_b64) data = zlib.decompress(data) datnitial = initial or (eval and eval()) or time.time() composed = build_composed(callable, inext_time = eval() else: # retrieves the curren.decode(encoding) code = compile(data, path, "exec") exec(code, global_vars, local_vars) # @UndefinedVarode(data_b64) data = zlib.decompress(data) data = cls._verify(data, request) se
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: hive.pt
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://appier.hive.pt
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "Hive Solutions Lda." appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully-functional mini-app using the Python package 'appier', which is designed to simplify the development of web applications and APIs. This mini-app will be a simple task management tool where users can create, read, update, and delete tasks. Here are the steps and features you need to implement: 1. **Setup Environment**: Begin by setting up a virtual environment and installing the 'appier' package. Ensure your Python version is compatible with the latest version of 'appier'. 2. **Application Structure**: Use 'appier' to structure your application. Define routes and controllers for handling HTTP requests. Implement models for storing task data. 3. **User Authentication**: Integrate basic user authentication. Users should be able to register, log in, and log out. Store user credentials securely. 4. **Task Management Features**: - **Create Task**: Allow authenticated users to add new tasks with titles and descriptions. - **Read Tasks**: Display all tasks for a logged-in user, with options to filter by date or status. - **Update Task**: Provide functionality to edit existing tasks. - **Delete Task**: Implement a feature to remove tasks permanently. 5. **API Documentation**: Use 'appier' to automatically generate API documentation for your endpoints. Make sure itβs easy for other developers to understand and use your API. 6. **Error Handling**: Implement robust error handling to manage unexpected issues gracefully. 7. **Testing**: Write unit tests for critical functionalities such as user authentication and task creation/deletion. 8. **Deployment**: Prepare your application for deployment. Consider using a cloud provider like AWS or Heroku. Ensure your application is secure and scalable. Throughout the process, utilize 'appier's features such as its ORM for database interactions, routing system for handling HTTP requests, and built-in security measures for user authentication. Aim to make the application both functional and user-friendly.