LLM Vulnerability Defense Through Defense-Score Model Selection
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Solution Overview
Problem
Large language models (LLMs) are susceptible to adversarial attacks that manipulate them into generating responses containing vulnerabilities such as personal identification numbers (PINs), passwords, user personal data, and proprietary information, posing a security risk.
Innovation Solution
A vulnerability defense system that includes a vulnerability assessment module to identify potential vulnerabilities in prompts, determines a vulnerability defense score for each LLM using a Bayesian hierarchical model, and selects the most resistant LLM to generate a response, utilizing databases like CVE and NVD for reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used to generate responses to user prompts, then productivity and response generation capability are improved, but vulnerability to adversarial attacks increases
Solution Approach 1:
The system performs preliminary vulnerability assessment of prompts before they are processed by LLMs. The vulnerability detection module analyzes prompts in advance to identify potential adversarial patterns, and the vulnerability defense score is calculated beforehand to determine which LLM should process the prompt, preventing vulnerabilities from being generated in the first place
Solution Approach 2:
The patent introduces an intermediary vulnerability defense system between user prompts and LLM responses. This intermediary layer includes the vulnerability detection module, LLM selection module, and vulnerability defense score calculation, which mediates the interaction by selecting the most appropriate LLM based on vulnerability scores rather than directly feeding prompts to any LLM
2Productivity
If multiple LLMs are used to increase response quality, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the LLM selection process into distinct functional modules: vulnerability detection module, vulnerability defense score calculation module, and LLM selection module. Each module handles a specific aspect of the multi-LLM coordination, making the overall complex system manageable through modular architecture
Solution Approach 2:
The patent uses vulnerability defense scores as a key parameter to differentiate and select between multiple LLMs. By calculating and comparing vulnerability defense scores for different LLMs, the system automatically selects the most appropriate model without requiring complex manual configuration or rule-based decision logic
Data Source
AI summary
A method for providing vulnerability defenses of LLMs to secure against generating responses to prompts that include vulnerabilities is provided. The method includes receiving a request including a prompt to be provided to a plurality of LLMs for generating a prediction of a response to the prompt, identifying, based on the prompt, one or more potential vulnerabilities associated with the plurality of LLMs generating the prediction of a response to the prompt, and determining, based on the identified one or more potential vulnerabilities, a vulnerability defense score associated with each of the plurality of LLMs. The vulnerability defense score includes an indication of a resistance of an LLM to generating a prediction of a response including one or more vulnerabilities. The method thus includes selecting, based on the vulnerability defense score, one of the plurality of LLMs for generating the prediction of a response to the prompt.