AI Analysis
Final verdict: SUSPICIOUS
The package shows signs of potential misuse due to the use of eval() with user input, which can lead to code injection. However, it does not exhibit other typical malicious behaviors.
- High obfuscation risk due to eval() usage
- New or less active author profile
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating no direct system command risks.
- Obfuscation: The use of eval() function with user input suggests potential for code injection and obfuscation, indicating high risk.
- Credentials: No clear evidence of credential harvesting patterns detected.
- Metadata: The author has only one package, which might indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
rt EvalSuite suite = builder.eval("What is your return policy?", expect="return") assert isins
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository vamsiramakrishnan/adk-fluent appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "adk-fluent contributors" 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 adk-fluent
Create a Python-based chatbot application named 'FluentBot' using the 'adk-fluent' package. This application will serve as an interactive tool for users to engage in conversations, retrieve information, and perform simple tasks. Here’s a step-by-step guide on how to develop this application: 1. **Setup Environment**: Begin by setting up your Python development environment. Ensure you have Python installed and create a virtual environment. Install the necessary packages including 'adk-fluent', which provides a fluent builder API for Google's Agent Development Kit (ADK). 2. **Define Core Functionality**: Define the core functionalities of your chatbot. These include initiating conversations, understanding user inputs, and generating appropriate responses. Use 'adk-fluent' to streamline the creation of intents, entities, and training phrases that are essential for your bot's conversational capabilities. 3. **Integrate with Dialogflow**: Utilize 'adk-fluent' to integrate your chatbot with Dialogflow, Google's platform for building conversational interfaces. This integration will allow your chatbot to leverage Dialogflow's natural language processing (NLP) capabilities. 4. **Develop Conversational Flows**: Design various conversational flows within your chatbot. For example, create a flow where the bot can answer FAQs about a product, provide weather updates based on location input from the user, or assist with scheduling appointments. 5. **Implement Custom Actions**: Implement custom actions within your chatbot that can perform specific tasks such as sending emails, setting reminders, or even making payments through a secure API integration. Use 'adk-fluent' to define these actions and ensure they are triggered appropriately based on user interactions. 6. **Testing and Iteration**: Thoroughly test your chatbot across different scenarios to ensure it behaves as expected. Pay special attention to error handling and fallback mechanisms. Use feedback from testing sessions to refine and improve the chatbot's performance. 7. **Deployment**: Once satisfied with the chatbot's functionality, deploy it to a server or cloud environment where it can be accessed via a web interface, mobile app, or other communication channels. 8. **Monitoring and Maintenance**: Set up monitoring tools to track the chatbot's performance and user engagement. Regularly update the chatbot with new features, fix bugs, and improve its conversational abilities based on ongoing user interactions and feedback. Throughout the development process, leverage 'adk-fluent' to simplify and enhance the creation of your chatbot's conversational logic and integrations.