LLM Output Redaction Against Privacy Attacks
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Solution Overview
Problem
Existing large language model (LLM) services are vulnerable to privacy attacks, hallucinations, and the dissemination of toxic or irrelevant content, with retraining to forget confidential information being resource-intensive and imperfect, and new hacking methods exploiting model vulnerabilities.
Innovation Solution
Implementing a processor-based intermediary system that monitors and redacts LLM outputs using a data sensitivity taxonomy and AI/ML models to determine and manage client privilege levels, applying techniques like data augmentation and token masking to protect sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an AI/ML model is retrained to unlearn confidential information, then privacy protection is improved, but model performance deteriorates and resource consumption increases
Solution Approach 1:
The patent introduces an intermediary privacy protection tool that sits between the LLM service and client networks. This tool monitors outputs, detects sensitive information using AI/ML models and data sensitivity taxonomy, and redacts confidential data without requiring retraining of the main LLM. The intermediary approach achieves privacy protection while preserving the original model's performance and avoiding resource-intensive retraining processes.
2Reliability
If an AI/ML model is retrained to unlearn confidential information, then privacy protection is improved, but resource consumption increases
Solution Approach 1:
The privacy protection tool serves as a resource-efficient intermediary that handles privacy protection tasks separately from the main LLM. It uses lighter-weight AI/ML models for detecting sensitive information and applies rule-based redaction techniques, avoiding the need to retrain the large LLM itself. This significantly reduces computational resources and energy consumption compared to full model retraining.
Solution Approach 2:
Instead of retraining the entire LLM model to unlearn information, the system applies partial action by only processing and redacting specific outputs that contain sensitive information. The privacy protection tool selectively monitors and redacts confidential data in responses without requiring comprehensive model retraining, thereby reducing resource consumption while maintaining effective privacy protection.
3Reliability
If an AI/ML model is retrained to unlearn confidential information, then direct privacy attacks are prevented, but indirect privacy attacks through contextual queries remain effective
Solution Approach 1:
The privacy protection tool implements feedback mechanisms by continuously monitoring LLM outputs and using AI/ML models to detect sensitive information patterns. The system learns from detected sensitive information types and adjusts its redaction strategies accordingly. This feedback loop enables the tool to identify and redact both direct and indirect references to confidential data, including contextual queries that attempt to bypass security measures.
Solution Approach 2:
The intermediary tool provides an additional layer of security between the LLM and users, specifically designed to detect and redact sensitive information in outputs. This mediator can identify indirect privacy attacks by analyzing the contextual meaning of responses, not just keyword matching. The tool redacts confidential information regardless of whether it was directly trained to forget or through contextual inference, thereby preventing both direct and indirect privacy attacks.
Data Source
AI summary
A system for securing a large language model (LLM) service against LLM privacy attacks. The system may comprise a processor that executes instructions that cause the processor to: interface with each output of the LLM service and each client network of the LLM service; monitor each output of the LLM to detect at least one client query textual response; detect and redact the at least one client query textual response according to an evaluation of that response and at least one current client privilege level that is assigned to at least one target client network account to which the at least one client query textual response is directed; and transmit a result of the redacting to a client device of the at least one target client network account.