Dual-LLM Attack Evaluation for Security Risk Detection
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Solution Overview
Problem
Existing technologies lack effective mechanisms to evaluate and mitigate the generation of incorrect or inappropriate information by large language models (LLMs), particularly generative pre-trained transformer (GPT) models, which can lead to security breaches and ethical concerns.
Innovation Solution
A computer-implemented method and system for evaluating attacks on LLMs, specifically GPT models, by inputting attack data to a first LLM, receiving response data, and evaluating this response data using a second LLM configured to assess LLM attack response data, thereby determining the severity of the attack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used to process user queries and generate responses, then productivity and information processing capability are improved, but security risks and potential exposure of confidential data increase
Solution Approach 1:
The patent introduces an intermediary evaluation system consisting of a vulnerability evaluator and a response evaluator that mediates between the LLM and user queries. The vulnerability evaluator assesses whether a query attempts to exploit known vulnerabilities, and the response evaluator determines if the LLM's response inadvertently exposes confidential information. This intermediary layer enables secure information processing by filtering and evaluating interactions without preventing the LLM from performing its core productivity-enhancing functions.
2Measurement precision
If LLMs ingest more data to improve accuracy, then information quality is improved, but reliability deteriorates because LLMs may generate false information or hallucinate
Solution Approach 1:
The patent implements a feedback mechanism where the evaluation system provides assessments back to the LLM interaction process. The vulnerability evaluator feedback helps adjust query processing to avoid exploiting vulnerabilities, and the response evaluator feedback informs whether generated responses maintain reliability. This feedback loop enables the system to continuously improve information quality while maintaining reliability by identifying and correcting hallucinations or false information generation patterns.
3Loss of time
If LLMs are evaluated using traditional testing methods, then development time is reduced, but evaluation precision is insufficient to detect subtle security vulnerabilities and inappropriate content
Solution Approach 1:
The patent implements a self-service evaluation approach where the LLM itself is used to evaluate its own responses and vulnerability exposures through the evaluation system. The response evaluator leverages the LLM's language understanding capabilities to assess whether its own outputs contain inappropriate content or expose confidential information. This self-service mechanism enables comprehensive security and quality evaluation without requiring extensive external testing resources, thus reducing evaluation time while maintaining high detection precision through the LLM's inherent linguistic expertise.
Data Source
AI summary
A computer implemented method of evaluating attacks on a first large language model (LLM). The method comprises: inputting attack data to the first LLM; receiving attack response data from the first LLM in response to the inputted attack data; inputting the attack response data to a second LLM configured to evaluate LLM attack response data; and receiving an evaluation of the attack response data from the second LLM.