AI Analysis
Final verdict: SAFE
The package exhibits low risks across all categories with no clear signs of malicious intent. While the metadata suggests some uncertainty due to the author's limited presence on PyPI, there is insufficient evidence to suggest a supply-chain attack.
- Low network, shell, obfuscation, and credential risks.
- Metadata raises minor suspicion but does not confirm any malicious behavior.
Per-check LLM notes
- Network: The observed network call is likely for fetching resources and does not inherently indicate malicious activity.
- Shell: No shell execution patterns were detected, indicating low risk in this aspect.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting no immediate risk related to secret or credential theft.
- Metadata: The author has a single package and uses a very common email domain, which raises some suspicion but not enough to conclusively determine malice.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3946 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
72 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
!= "": response = requests.get(source["url"]) response.raise_for_status()
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
score 3.0
Suspicious email domain flags: Very short email domain: qq.com
Very short email domain: qq.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "胡伟" 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 aisuite4cn
Create a Chinese Language Assistant Application using the Python package 'aisuite4cn'. This application will serve as a versatile tool for users to interact with various Large Language Models (LLMs) available in China, offering features such as text generation, translation, summarization, and more. The application should include a user-friendly interface and support multiple input/output formats to cater to diverse needs. Steps to Develop the Application: 1. **Setup Project Environment**: Initialize a new Python project and install the required dependencies including 'aisuite4cn'. 2. **Design User Interface**: Create a simple but effective UI where users can input their queries and receive responses from the LLMs. Consider using a library like Tkinter for desktop applications or Flask for web-based interfaces. 3. **Implement Core Functionality**: - **Text Generation**: Allow users to generate text based on prompts they provide. Utilize 'aisuite4cn' to connect with different LLMs and retrieve results. - **Translation Services**: Integrate translation capabilities between Chinese and other major languages. Use 'aisuite4cn' to facilitate these translations through its supported LLMs. - **Summarization Tools**: Enable users to input long texts and get summarized versions. Again, leverage 'aisuite4cn' to perform this task efficiently. 4. **Enhance User Experience**: Add features such as history tracking of previous interactions, error handling for better user experience, and possibly allow users to switch between different LLMs within the app. 5. **Testing and Deployment**: Thoroughly test the application for functionality, performance, and usability. Once satisfied, deploy the application either as a downloadable executable or a web service. How 'aisuite4cn' is Utilized: - **Initialization**: Import 'aisuite4cn' at the beginning of your scripts and initialize it with necessary configurations (API keys, model selection, etc.). - **Interaction**: Use 'aisuite4cn' functions to communicate with LLMs. For example, call specific methods for text generation, translation, and summarization. - **Error Handling**: Implement error handling around 'aisuite4cn' calls to manage exceptions gracefully and provide meaningful feedback to users.