AI Model Vulnerability Testing With Prompt and Filter Variations
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Solution Overview
Problem
Existing AI models, particularly large language models (LLMs), are susceptible to vulnerabilities such as prompt injection, prompt leakage, toxicity, personally identifiable information (PII) leakage, hallucinations, and sponge attacks, which can lead to unintended, wasteful, or malicious outputs, affecting user experience and resource utilization.
Innovation Solution
A system that generates and tests multiple prompt variations and filter variations using AI models to identify and mitigate vulnerabilities in LLMs, including generating a report on vulnerability testing and mitigation effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional vulnerability testing methods are used for AI models, then testing can be performed, but computational resources are excessively consumed and testing efficiency is low
Solution Approach 1:
The patent applies self-service by using AI models to automatically generate vulnerability test cases and evaluate their effectiveness against target models. The system leverages the AI models' own capabilities to create and assess security vulnerabilities, eliminating the need for extensive manual testing and reducing computational resource consumption while maintaining high testing efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the AI model evaluates the effectiveness of generated test cases against the target model's responses. This feedback loop allows the system to iteratively improve test case generation and selectively focus computational resources on the most impactful vulnerability testing scenarios, thereby optimizing the balance between testing thoroughness and resource utilization.
2Reliability
If comprehensive vulnerability testing is performed to ensure security, then model reliability improves, but testing time and computational cost increase
Solution Approach 1:
The patent applies preliminary action by using AI models to pre-generate and pre-evaluate vulnerability test cases before actual security assessment. The system prepares a curated set of potential vulnerability scenarios in advance, allowing the security evaluation process to proceed more quickly by testing only the most promising candidates rather than exhaustively testing all possible scenarios.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the scope and complexity of vulnerability testing based on the AI model's assessment of risk parameters. The system modifies testing parameters such as the number of test cases, depth of analysis, and computational intensity according to the identified vulnerability landscape, thereby optimizing the balance between security thoroughness and testing time consumption.
Data Source
AI summary
The present disclosure provides techniques for determining and mitigating AI model vulnerabilities. A processing device generates, via a first AI model, a plurality of prompt variations based on an indication of a vulnerability. The processing device determines that a second AI model is vulnerable to the vulnerability based on at least one prompt variation in the plurality of prompt variations. The processing device generates a plurality of filter variations based on a plurality of filters and the at least one prompt variation. The processing device tests the plurality of filter variations and the at least one prompt variation on the second AI model. The processing device generates, based on the testing, a report indicative of an effectiveness of the plurality of filter variations in mitigating the vulnerability with respect to the second AI model.