LLM Risk Mitigation via Kernel-Level Call Interception
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models (LLMs) pose risks due to potential misuse, generation of deceptive and inaccurate content, perpetuation of biases, and privacy concerns, necessitating effective risk mitigation strategies.
Innovation Solution
Implementing a networked environment with a computing environment, LLM security service, and risk mitigation code to identify and modify LLM applications, using pre-sink method techniques and kernel-level interception to manage interactions, ensuring secure and accurate outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs generate highly realistic and coherent text, then text quality and coherence are improved, but accuracy and reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary verification system that sits between the LLM generation process and the final output. This intermediary layer checks generated content against knowledge bases, fact-checks information, and validates coherence without compromising the LLM's ability to generate realistic text. The intermediary acts as a mediator that preserves text quality while ensuring accuracy.
2Adaptability or versatility
If LLMs are used to expand AI capabilities, then functionality and utility are improved, but security and data protection worsen
Solution Approach 1:
The patent segments the AI system into multiple independent components: the LLM generation module, the security verification module, the data protection layer, and the output validation module. Each segment handles specific functions independently, allowing the system to expand functionality through the LLM while security concerns are addressed by dedicated separate modules that do not compromise the core generative capabilities.
3Productivity
If LLMs generate content quickly, then productivity is improved, but risk of misuse and erroneous output worsens
Solution Approach 1:
The patent implements preliminary actions by pre-training the LLM on high-quality, verified data and pre-loading knowledge bases with accurate information before generation occurs. Additionally, verification rules and safety constraints are pre-configured into the system. This preliminary preparation enables the LLM to generate content quickly while reducing the need for extensive post-generation checking, thus maintaining both speed and reliability.
Data Source
AI summary
Disclosed are various approaches for large language model (LLM) application risk mitigation. A large language model (LLM) application along with a LLM risk mitigation program can be deployed. A system call for the LLM application is intercepted, in which the system interacts with a network LLM service using the LLM risk mitigation program. An LLM risk mitigation action is executed and modifies the system call using the LLM risk mitigation program based at least in part on a packet from the system call.