External LLM Security Monitoring for Multi-Turn Attack Defense
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Solution Overview
Problem
Large Language Models (LLMs) trained on vast internet datasets pose risks of sensitive information leakage and potential malicious skill teaching due to evolving attack strategies, necessitating advanced security measures.
Innovation Solution
A method and system for monitoring, detecting, and defending against attacks on LLMs, operating externally to the LLM, which includes cybersecurity monitoring and protection mechanisms such as firewalls, intrusion detection, and AI-based defense strategies to identify and mitigate threats like prompt hacking and adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are trained on massive internet datasets to provide comprehensive knowledge, then the model's knowledge coverage and capability are improved, but the risk of sensitive information leakage increases
Solution Approach 1:
The patent introduces an external security monitoring system as an intermediary between the user and the LLM. This mediator monitors inputs and outputs in real-time, detecting sensitive information and malicious content without requiring changes to the LLM's training data or architecture, thus preserving knowledge coverage while mitigating leakage risks
Solution Approach 2:
The patent segments the security function from the LLM core model by implementing an external monitoring system. This separation allows the LLM to maintain its comprehensive knowledge base while the independent security module handles sensitive information detection and filtering, resolving the contradiction between knowledge breadth and security
2Ease of operation
If LLMs encapsulate broad human knowledge to assist users, then the model's usefulness is improved, but the potential to teach malicious skills increases
Solution Approach 1:
The patent implements a feedback mechanism where the external monitoring system continuously analyzes LLM outputs and provides real-time detection of malicious content. When harmful skills or information are detected, the system triggers defensive actions to block or neutralize the output, allowing the LLM to remain helpful while preventing malicious teaching
Solution Approach 2:
The patent applies preliminary anti-action by pre-configuring the external monitoring system with attack patterns and malicious content signatures. Before the LLM can generate harmful content, the monitoring system detects and neutralizes such outputs based on pre-established security rules, preventing malicious skill teaching while preserving legitimate assistance
3Reliability
If safety controls are implemented within LLMs to prevent attacks, then the model's security is improved, but the complexity of the system increases
Solution Approach 1:
The patent places the security control logic in an external monitoring system rather than within the LLM itself. This intermediary approach improves security by isolating attack detection from the core model while avoiding the complexity of integrating security mechanisms into the LLM's architecture, maintaining system simplicity
4Measurement precision
If external monitoring is implemented to detect attacks on LLMs, then the security detection capability is improved, but the computational resources and time required increase
Solution Approach 1:
The patent applies partial action by implementing monitoring that focuses specifically on detecting attack patterns and malicious content rather than analyzing every aspect of LLM interactions. This targeted approach improves detection accuracy for security threats while minimizing the time and computational resources required compared to comprehensive monitoring
Data Source
AI summary
Large Language Model (LLM) security includes monitoring an LLM; detecting an attack on the LLM and defining an attack type of a plurality of attack types based on the monitoring, providing a notification of the attack; and causing a defense to the attack based on the attack type. Advantageously, the security can be configured to be executed between a user outside of the LLM. Further, the security can be configured to defend against multi-turn attacks.