LLM Agent Consent Control for Secure Group Communication
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Solution Overview
Problem
Existing AI systems with multiple large language model (LLM) chatbots face challenges in ensuring security, privacy, and data use restrictions due to lateral data sharing, non-deterministic behavior, and lack of user control over data access and communication between agents, particularly in consumer scenarios involving third-party agents and plugins.
Innovation Solution
Implementing a security module that handles customer authentication, authorization, and in-context customer consent, using industry standards like OpenID Connect and OAuth2.0, to control and transparently manage data access and communication between LLM agents, with features like private mode and user-controlled selection of agents, and intermediating all communication to enforce security, privacy, and data use restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lateral data sharing is enabled between multiple LLM agents, then system productivity and collaborative capability are improved, but security and privacy risks increase due to unauthorized data exposure
Solution Approach 1:
The patent introduces a security module as an intermediary between LLM agents and data resources. This module authenticates agents, authorizes data access, and mediates communication protocols. The security module intercepts and validates all data sharing operations, allowing productive collaboration while preventing unauthorized access through layered security controls.
Solution Approach 2:
The system segments data access rights and communication channels between different LLM agents. Each agent receives specific access tokens and permission scopes that limit their data visibility and access capabilities. This segmentation enables functional collaboration while isolating security risks to individual agent boundaries.
2Adaptability or versatility
If non-deterministic behavior is allowed in LLM agents, then adaptability and creative problem-solving are improved, but system reliability deteriorates due to unpredictable actions
Solution Approach 1:
The security module implements continuous feedback loops that monitor agent behavior, data access patterns, and communication protocols. When agents exhibit unpredictable or policy-violating behavior, the system receives real-time feedback and automatically adjusts permissions, blocks operations, or alerts administrators. This feedback mechanism maintains adaptability while ensuring reliable security posture.
3Ease of operation
If user control over data access is limited, then system ease of operation is improved, but user privacy and data security sovereignty are worsened
Solution Approach 1:
The system implements self-service authentication and authorization mechanisms where users can manage their own data access permissions through intuitive interfaces. Users can grant or revoke agent access to specific data resources without requiring technical expertise. The security module automatically enforces user-defined policies, providing ease of operation while maintaining complete user sovereignty over privacy and security settings.
Data Source
AI summary
Security, privacy, and data use restrictions in group communication with multiple large language model (LLM) chatbots or agents are provided. In-context user consent is obtained for operations performed by LLM agents on behalf of the user as and when needed. A first message directed to a first LLM agent is received via a user interface (UI). Based on a determination that the first message is to invoke a second LLM agent, a consent request for consent of a user to invoke the second LLM agent is provided via the UI. Upon receiving the consent of the user to invoke the second LLM agent, the second LLM agent is invoked within the context of the UI. In some examples, a command to enter private mode may be received to limit the communication in the private mode only selected LLM agents.