aiohttp

v3.14.0 safe
4.0
Medium Risk

Async http client/server framework (asyncio)

🤖 AI Analysis

Final verdict: SAFE

The package aiohttp v3.14.0 is assessed as safe with a low risk score due to its expected network-related functionality and lack of indications of malicious activities such as shell execution or credential harvesting.

  • Low network and shell risks
  • No evidence of credential harvesting
Per-check LLM notes
  • Network: Expected for a networking library like aiohttp, but no specific calls indicate unusual risk.
  • Shell: Low risk as shell execution is not typical behavior for aiohttp.
  • Obfuscation: The usage of base64 decoding is common for various purposes and does not necessarily indicate malicious activity.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: setup.cfg
  • 1 test file(s) detected (e.g. test_utils.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Docs: Changelog" -> https://docs.aiohttp.org/en/stable/changes.html
  • 2 documentation file(s) (e.g. client_middleware_cookbook.py)
  • Detailed PyPI description (5693 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 893 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in aio-libs/aiohttp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • try: decoded = base64.b64decode( encoded_credentials.encode("ascii"), valida
  • "base64": return base64.b64decode(data) elif encoding == "quoted-printable":
  • if not key or len(base64.b64decode(key)) != 16: raise HTTPBadRequest(text=f"Han
  • try: decoded = base64.b64decode(encoded_creds).decode() username, password = dec
✓ 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: aiohttp.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aio-libs/aiohttp 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 aiohttp 
Create a fully-functional asynchronous web scraper using Python's aiohttp package. This tool will be designed to efficiently scrape multiple websites concurrently, providing a robust solution for gathering data from various sources. Here’s a detailed breakdown of the project requirements and features:

1. **Project Overview**: Your task is to develop a web scraping application that leverages aiohttp for asynchronous HTTP requests. This application will allow users to input a list of URLs and scrape specific data (e.g., titles, meta descriptions, image links) from each webpage.

2. **Core Features**:
   - **Asynchronous Request Handling**: Use aiohttp to handle multiple HTTP GET requests asynchronously, allowing your scraper to fetch data from several sites simultaneously without blocking.
   - **Scraping Specific Data**: Implement functionality to extract specific types of content from HTML pages (e.g., <title> tags, <meta> tags, <img> tags).
   - **Concurrency Control**: Allow users to specify the number of concurrent requests they want to make, optimizing performance based on their needs.
   - **Error Handling**: Implement robust error handling mechanisms to manage issues like network errors, timeouts, and invalid responses gracefully.

3. **Implementation Steps**:
   - **Setup Environment**: Install necessary packages including aiohttp and BeautifulSoup (or another HTML parser).
   - **Define Data Extraction Functions**: Create functions that use aiohttp to fetch HTML content and then parse it using BeautifulSoup or similar tools to extract desired information.
   - **Asynchronous Request Management**: Utilize aiohttp's async capabilities to manage request concurrency effectively.
   - **User Interface**: Develop a simple command-line interface where users can input URLs and specify which types of data to scrape.
   - **Output Results**: Display scraped data in a readable format, possibly storing results in a CSV file or database for further analysis.

4. **Additional Suggestions**:
   - **Rate Limiting**: Implement rate limiting to avoid overwhelming servers with too many requests in a short period.
   - **Session Management**: Use aiohttp sessions to improve efficiency when making multiple requests to the same server.
   - **Logging and Monitoring**: Add logging to track progress and debug issues easily.

By following these guidelines, you’ll create a powerful yet user-friendly web scraping tool that demonstrates the power of aiohttp for asynchronous web interactions.