AI Analysis
Final verdict: SUSPICIOUS
The package aiomisc v18.0.24 has a moderate risk score due to incomplete metadata and a potentially new or inactive maintainer account, despite showing no signs of malicious activity such as obfuscation, shell execution, or credential harvesting.
- Metadata risk due to incomplete maintainer information
- Potential supply chain risk due to new or inactive maintainer account
Per-check LLM notes
- Network: The network call patterns suggest the package is likely using UDP sockets and HTTP(S) requests for asynchronous operations, which aligns with its probable functionality.
- Shell: No shell execution patterns were detected.
- 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 account seems new or inactive, which could indicate potential risk.
Package Quality Overall: Medium (6.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Classifier: Framework :: Pytest
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.aiomisc.com/Detailed PyPI description (11721 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 :: Typed354 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in aiokitchen/aiomiscSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
cket.SOCK_DGRAM) self.socket.connect(self.socket_addr) self.socket.setblocking(False)mily ) return aiohttp.ClientSession(connector=connector) async def _do_send( self,
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: mosquito.su>
Suspicious Page Links
score 4.0
Found 2 suspicious link(s) on the package page
Non-HTTPS external link: http://graphiteapp.orgNon-HTTPS external link: http://semver.org/
Git Repository History
Repository aiokitchen/aiomisc 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 aiomisc
Create a fully functional asynchronous web scraper using Python and the 'aiomisc' package. Your application should scrape multiple websites concurrently, extract specific data (e.g., article titles and URLs from news sites), and store this information in a structured format such as JSON or a SQLite database. Additionally, implement logging to track the scraping process and any errors encountered during execution. Use 'aiomisc' to manage the asynchronous tasks efficiently and handle exceptions gracefully.
Steps to complete the project:
1. Set up your development environment with Python and install the required packages ('aiomisc', 'aiohttp' for HTTP requests).
2. Define the target websites you wish to scrape.
3. Create an asynchronous function that will perform the scraping task for each website.
4. Utilize 'aiomisc' to run these scraping functions concurrently.
5. Implement error handling within your scraping functions to deal with potential issues like network errors or changes in website structure.
6. Store the scraped data into a SQLite database or output it as JSON files.
7. Add logging to monitor the scraping progress and capture any errors.
8. Optimize your code for efficiency and readability.
Suggested Features:
- Command-line arguments to specify which websites to scrape.
- Support for resuming interrupted scraping sessions.
- Option to limit the number of concurrent connections to avoid overwhelming servers.
- Ability to specify custom selectors for different types of content on various websites.