🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and metadata risks, raising concerns about its true intentions. While there are no direct signs of malicious activities such as shell execution or credential harvesting, the combination of these factors suggests potential misuse.

High obfuscation risk indicating attempts to hide code's purpose
Suspicious metadata with non-HTTPS links and incomplete author details

Per-check LLM notes

Network: The observed network patterns are typical for packages that perform HTTP requests asynchronously but may indicate potential data exfiltration if the URLs are not controlled by the user.
Shell: No shell execution patterns were detected.
Obfuscation: The observed pattern suggests an attempt to hide the true purpose of the code, which is common in malicious scripts but could also be used for legitimate purposes like data encryption.
Credentials: No clear signs of credential harvesting detected.
Metadata: Suspicious non-HTTPS link and author details lacking indicate potential risks.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
6 test file(s) detected (e.g. base.py)
Classifier: Framework :: Pytest

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (14459 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
Type checker (mypy / pyright / pytype) referenced in project
61 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 63 commits in Polandia94/aiointercept
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

PrivateUsage] return aiohttp.ClientSession(connector=_BypassConnector(resolver=_BypassResolver()))
True}) async with aiohttp.ClientSession() as session: resp = await session.get(url)
peat=True) async with aiohttp.ClientSession() as session: for _ in range(n):
peat=True) async with aiohttp.ClientSession() as session: for _ in range(n // batch):
"qty": 3} async with aiohttp.ClientSession() as session: for _ in range(n):

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ck(self, m): body = b"\x00\x01\x02\x80\x81\x82\x83\x84\x85" def callback(url, **kwargs): return C

✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:54321

✓ Git Repository History

Repository Polandia94/aiointercept appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aiointercept

Develop a fully-functional mini-application named 'RequestRouter' using the Python package 'aiointercept'. This application will serve as a request routing tool for developers testing their web services locally. The goal is to demonstrate how 'aiointercept' can be used to intercept HTTP requests and route them through a local test server, allowing developers to simulate different network conditions and responses without needing to hit external APIs directly.

Step-by-Step Instructions:
1. **Setup Environment**: Ensure Python 3.8+ is installed along with virtualenv. Create a new virtual environment and install necessary packages including 'aiointercept', 'aiohttp', and 'pytest'.
2. **Project Structure**: Define a clean project structure with directories for tests, configuration files, and the main application code.
3. **Core Functionality**:
   - Implement a class named `RequestRouter` which initializes a local test server using `aiointercept`.
   - Within this class, create methods to start and stop the server, register routes, and define response behaviors for each registered route.
4. **Feature Implementation**:
   - **Route Registration**: Allow users to dynamically register routes (URL patterns) and associate them with specific response codes, content types, and body payloads.
   - **Response Customization**: Provide options to customize responses based on headers, query parameters, and path parameters.
   - **Logging and Monitoring**: Integrate logging capabilities to track incoming requests and outgoing responses, and include basic monitoring of request counts per route.
5. **Testing**:
   - Write unit tests using pytest to ensure the application behaves as expected under various conditions.
6. **Documentation**: Create comprehensive documentation detailing how to install the application, its usage, and a guide for extending its functionality.
7. **Deployment**: Package the application as a standalone executable or a Docker image for easy deployment.

The 'aiointercept' package is crucial here for setting up the local test server and routing requests through it. It allows for realistic simulation of HTTP interactions, making it invaluable for testing scenarios where direct access to live APIs is impractical or undesirable.