AI Data Filters for Prompt Injection and Content Leakage
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Solution Overview
Problem
Existing generative AI models, such as those based on GPT architecture, are vulnerable to prompt injection attacks and sensitive data leakage, making it difficult to safeguard against harmful or non-compliant outputs.
Innovation Solution
An AI-based filter apparatus with input and output filters, utilizing pre-trained language models and additional layers, to assess user queries and model responses for harmful or restricted content, preventing their transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are trained on vast amounts of data using large language models, then the model's knowledge and language processing capabilities are improved, but the model becomes vulnerable to prompt injection attacks and sensitive data leakage
Solution Approach 1:
The patent introduces an AI-based filter apparatus as an intermediary component between user queries and the generative AI model. This filter uses pre-trained language models and additional layers to detect and block harmful prompts before they reach the main model, thereby protecting the model's security while preserving its language processing capabilities
Solution Approach 2:
The filter apparatus performs preliminary security checks by analyzing user queries before they are processed by the generative AI model. The pre-trained language models in the filter detect potential prompt injection attacks and harmful content in advance, preventing these malicious inputs from reaching the main model
2Reliability
If existing filters are used to moderate content, then some level of protection is provided, but they are not optimized for detecting harmful content in third-party services utilizing GPT models
Solution Approach 1:
The patent changes the parameters and architecture of the filtering system by incorporating pre-trained language models specifically optimized for detecting harmful content in the context of third-party GPT model services. The filter uses additional layers trained on specific threat patterns, enabling it to adapt to the unique challenges of third-party service environments
Solution Approach 2:
The filter apparatus is designed to be integrated into existing third-party services that utilize GPT models, allowing these services to self-protect without requiring fundamental changes to their core functionality. The filter operates as a plug-in security layer that serves the existing service architecture
3Measurement precision
If the filter apparatus uses pre-trained language models and additional layers for content assessment, then detection accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the filtering system into distinct functional components: pre-trained language models for understanding query intent, additional layers for detecting harmful patterns, and a decision-making component that determines whether to block or allow queries. This segmentation allows each component to be optimized independently while working together to achieve high detection accuracy
Data Source
AI summary
An Artificial Intelligence (AI) based filter apparatus includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.