AI Analysis
The package shows minimal risk indicators with no detected network or shell vulnerabilities. However, the metadata risk due to sparse author information and non-secure links slightly elevates the overall score.
- Sparse author information
- Presence of non-secure links
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution detected, which is expected as executing system commands is typically unnecessary for a DNS resolver.
- Metadata: The author information is sparse and the presence of non-secure links raises some concern, but there's no strong evidence of malicious intent.
Package Quality Overall: Medium (7.4/10)
Test suite present — 3 test file(s) found
Test runner config found: conftest.py3 test file(s) detected (e.g. conftest.py)
Some documentation present
1 documentation file(s) (e.g. conf.py)Detailed PyPI description (3951 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
Classifier: Typing :: Typed52 type-annotated function signatures detected in source
Active multi-contributor project
6 unique contributor(s) across 91 commits in aio-libs/aiohttp-asyncmdnsresolverActive community — 5 or more distinct contributors
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
Found 3 suspicious link(s) on the package page
Non-HTTPS external link: http://example.comNon-HTTPS external link: http://xxx.local.Non-HTTPS external link: http://printer.local.
Repository aio-libs/aiohttp-asyncmdnsresolver appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 Python-based network discovery tool using the 'aiohttp-asyncmdnsresolver' package. This tool will enable users to discover services and devices on their local network via MDNS (Multicast DNS). Here’s a detailed breakdown of the project requirements and steps to build it: 1. **Project Overview**: Your goal is to develop a command-line tool that can scan a local network for services and devices using MDNS. The tool should be able to identify common service types like printers, file servers, and web servers. 2. **Setup Environment**: Begin by setting up your Python environment. Install necessary packages such as 'aiohttp', 'aiohttp-asyncmdnsresolver', and 'click' for command-line interface handling. 3. **Design the Core Functionality**: Use 'aiohttp-asyncmdnsresolver' to asynchronously resolve MDNS queries. This involves configuring the resolver to listen for specific service types (e.g., '_http._tcp') and gathering responses from the network. 4. **Implement Discovery Logic**: Write functions to initiate the discovery process. These functions should query the local network for specified service types and gather all responding devices/services into a list. 5. **Output Results**: Once the discovery process is complete, display the discovered devices/services in a user-friendly format. Include details such as hostnames, IP addresses, and service types. 6. **Add Command-Line Interface**: Utilize 'click' to create a simple yet powerful CLI. Users should be able to specify service types to search for and choose between different output formats (e.g., JSON, plain text). 7. **Enhanced Features**: - **Service Filtering**: Allow users to filter results based on keywords or specific criteria. - **Configuration Options**: Provide options for customizing the discovery process, such as setting timeouts or increasing the verbosity of the output. 8. **Testing and Documentation**: Ensure thorough testing of your application across various network conditions. Document the setup, usage, and customization options clearly for other developers and end-users.