AI Plugin Capability Declarations for Secure Parallel Execution
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Solution Overview
Problem
Existing AI systems with multiple plugins face challenges in managing plugin interactions, leading to unpredictable execution, increased response times, and insecure data exposure due to lack of capability declarations.
Innovation Solution
Implementing plugin configurations that define inputs and capabilities, enabling optimized execution order, parallel execution, and secure data handling by analyzing dependencies and capabilities to manage plugin interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple plugins are available to extend AI system capabilities, then the system can tackle more complex problems and operate more efficiently, but the execution becomes unpredictable and response times increase
Solution Approach 1:
The system performs preliminary analysis of plugin configurations to determine execution order and parallelization opportunities before actual execution. This includes analyzing capability declarations, input requirements, and dependency relationships upfront, so that the execution plan is optimized in advance rather than determined during runtime, reducing overall response time.
Solution Approach 2:
The patent segments the plugin execution process into distinct phases: configuration analysis, dependency resolution, execution ordering, and actual execution. By dividing the complex multi-plugin execution into manageable segments with clear boundaries, the system can optimize each phase independently and manage complexity effectively.
2Adaptability or versatility
If multiple plugins interact with one another, then the system can perform specific tasks and integrate with other technologies, but security risks increase due to lack of capability declarations
Solution Approach 1:
The system introduces capability declarations as an intermediary layer between plugins and the AI system. These declarations act as formal contracts that define what data each plugin can access and what operations it can perform. The intermediary validation mechanism checks these declarations to ensure security constraints are met, allowing plugin functionality while maintaining security control.
Solution Approach 2:
The system implements feedback mechanisms where capability declarations are analyzed and validated before plugin execution is permitted. The validation process provides feedback about security compliance, and only plugins that pass the security checks are allowed to execute. This creates a closed-loop security verification system.
Data Source
AI summary
Disclosed are methods for managing execution of plugins of a machine-learning based system. A plugin configuration defines inputs required by the plugin and capabilities provided by the plugin. Capabilities describe the plugin’s functionality, such as how the plugin affects the response, what type of content the plugin generates, etc. In some configurations, when responding to a prompt, a collection of relevant plugins is identified. Configurations of these plugins may be analyzed to optimize execution, including determining optimal execution order or enabling parallel execution. Plugin configurations may also be analyzed to improve security by conditionally preventing one plugin from accessing the output of another. Plugin configurations may also be used to inform a client what plugins will run and what results they may yield. This enables the client to optimize and streamline how the response is displayed.