AI Analysis
Final verdict: SUSPICIOUS
The package shows some level of concern due to missing repository information and sparse author details, which raises questions about its origin and maintenance. However, there are no direct indicators of malicious intent or activity.
- Metadata risk score of 5 out of 10 due to missing repository.
- Sparse author details.
Per-check LLM notes
- Network: The presence of network calls is common in packages that require external API access, but the specific implementation should be reviewed to ensure it does not lead to unauthorized data transfer.
- Shell: No shell execution patterns were detected, which is normal and indicates no immediate risk from this aspect.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The repository is not found, and the author details are sparse, indicating potential concerns but not strong evidence of malice.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
nt_id self._client = httpx.Client( base_url=self._base_url, headers=he
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: agentbouncr.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
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 agentbouncr
Create a mini-application named 'AI Governance Dashboard' using the Python package 'agentbouncr'. This application will serve as a user-friendly interface for managing and monitoring AI agents in various environments. The goal is to provide real-time insights into the behavior of these agents, ensuring they operate within predefined ethical and operational guidelines. Step 1: Set up your development environment. Ensure you have Python installed along with 'agentbouncr'. Use pip to install the package if necessary. Step 2: Design the application's architecture. It should include modules for agent registration, compliance checks, and reporting. Each module will interact with 'agentbouncr' to perform its tasks. Step 3: Implement the agent registration feature. Users should be able to register new AI agents by providing basic details such as name, type, and environment. Utilize 'agentbouncr' to securely store this information. Step 4: Develop the compliance check functionality. This feature will periodically assess whether registered agents adhere to specified governance policies. Leverage 'agentbouncr' to define and enforce these policies. Step 5: Create a reporting system. Provide users with comprehensive reports on agent performance and compliance status. These reports should highlight any deviations from policy and suggest corrective actions. Suggested Features: - Interactive dashboard for visualizing agent performance metrics. - Customizable alerting system for immediate notification of policy violations. - Detailed logs of all interactions between agents and the governance system. - User authentication and role-based access control to ensure data security. Utilization of 'agentbouncr': - For registering and managing agent metadata. - To implement and enforce governance policies through API calls. - For generating compliance reports based on real-time data analysis. Your final product should be a robust, scalable solution for governing AI agents, demonstrating the power and flexibility of 'agentbouncr'.