AI Analysis
Final verdict: SAFE
The package appears to be designed for web scraping and asset storage, with minimal risks identified. However, its novelty and lack of maintainer history warrant caution.
- Low network, shell, obfuscation, and credential risks.
- Moderate metadata risk due to newness and lack of maintainer history.
Per-check LLM notes
- Network: The use of requests.Session() with headers update is common for making HTTP requests but should be reviewed for destinations and purposes.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new and lacks maintainer history, with no git repository found.
Heuristic Checks
Outbound Network Calls
score 6.0
Found 4 network call pattern(s)
bose self._session = requests.Session() self._session.headers.update(DEFAULT_HEADERS)EFAULT_HEADERS sess = requests.Session() sess.headers.update(DEFAULT_HEADERS) for aimeout self._sess = requests.Session() self._sess.headers.update(DEFAULT_HEADERS) #imeout self._sess = requests.Session() self._sess.headers.update(DEFAULT_HEADERS) de
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: example.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "aax-vision-lib" 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 aax-vision-lib
Create a personalized educational content summarizer using the 'aax-vision-lib' Python package. This application will allow users to input a URL of any online educational resource such as a blog post, video tutorial, or lecture slides. The app will then scrape the provided URL, extract key visual and textual information, and generate a concise summary of the content. Here are the steps and features you need to implement: 1. **User Interface**: Design a simple web interface where users can enter a URL and submit it for processing. 2. **Content Scraping**: Utilize 'aax-vision-lib' to scrape the entered URL and gather all available media (images, videos) and text content. 3. **Content Analysis**: Apply natural language processing techniques to analyze the text data, identifying key points, summaries, and important quotes. 4. **Visual Content Processing**: Use 'aax-vision-lib' to process images and videos, extracting meaningful insights or descriptions that could complement the text summary. 5. **Summary Generation**: Combine the analyzed text and visual insights into a cohesive summary that captures the essence of the original content. 6. **Output Presentation**: Present the summary in a user-friendly format on the web interface, including relevant images and links back to the original sources. 7. **Optional Enhancements**: Consider adding features like sentiment analysis, keyword highlighting, and the ability to save summaries for future reference. This project leverages the versatile capabilities of 'aax-vision-lib' to handle various types of digital content, making it a powerful tool for anyone looking to quickly grasp the main ideas from complex educational resources.