LLM Risk Mitigation Code for Secure Network Model Interactions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models (LLMs) pose risks due to potential misuse, erroneous outputs, biased content generation, privacy concerns, and the inadvertent creation of sensitive information, which can contribute to societal issues and privacy breaches.
Innovation Solution
Implementing a networked environment with a computing environment, LLM security service, and risk mitigation code to identify and mitigate risks through packet filtering, bias mitigation, and other automated evaluations, using a private LLM to generate risk mitigation code and applying pre-sink method techniques to modify LLM applications and intercept system calls.
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 acts as a mediator between the LLM's text generation and the final output. This system includes fact-checking modules, citation verification, and cross-referencing mechanisms that validate the generated content before presentation, thereby maintaining coherence while improving accuracy through an intermediate validation layer.
Solution Approach 2:
The patent implements feedback loops where the generated text is continuously evaluated against multiple criteria including factual accuracy, source verification, and logical consistency. The system uses automated fact-checking, citation validation, and confidence scoring to provide feedback that guides text generation and correction, ensuring both coherence and reliability are maintained iteratively.
2Productivity
If LLMs are used to expand AI capabilities, then productivity and utility are improved, but security risks and misuse potential worsen
Solution Approach 1:
The patent applies preliminary action by implementing security measures before the LLM processes sensitive or potentially harmful inputs. This includes pre-processing filters, intent analysis, and risk assessment mechanisms that evaluate requests before they reach the main generation system, preventing misuse while preserving legitimate productivity-enhancing applications.
Solution Approach 2:
The patent segments the LLM system into multiple specialized modules with distinct security functions. This includes separate components for input validation, content filtering, fact-checking, and output verification, allowing each segment to focus on specific security aspects while maintaining overall system productivity and utility.
3Speed
If LLMs generate content quickly, then response speed is improved, but accuracy and factuality worsen
Solution Approach 1:
The patent applies partial action by implementing selective verification where not all generated content undergoes the full verification process. High-confidence, low-risk statements are output quickly with minimal verification, while uncertain or high-risk content receives more thorough fact-checking and source validation, balancing speed and accuracy based on the specific content characteristics.
Data Source
AI summary
Disclosed are various approaches for large language model (LLM) application risk mitigation. A large language model (LLM) application that interacts with a network LLM service can be identified. A portion of the LLM application can be provided as input to an LLM risk mitigation code generation function that outputs LLM-specific risk mitigation code. A runtime environment can be deployed to include a modified version of the LLM application that includes the LLM-specific risk mitigation code, a kernel-layer LLM risk mitigation program that can intercept LLM interaction system calls to apply the LLM-specific risk mitigation code, or any combination thereof.