LLM Prompt Evolution Using Pareto Selection for Security Balance
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Solution Overview
Problem
Existing enterprise applications face challenges in optimizing machine-generated prompts for large language models (LLMs) to balance performance and security objectives, as generating effective responses can conflict with preventing unauthorized use and unintended consequences.
Innovation Solution
A method and system utilizing an evolutionary algorithm approach with semantic mutation, pareto selection, and crossover mutation to optimize machine-generated prompts, ensuring a balance between performance and security objectives through a series of feedback mutators and pareto efficiency criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-generated prompts are optimized for performance to generate effective responses, then response quality is improved, but security risks increase due to potential unauthorized use and unintended consequences
Solution Approach 1:
The prompt optimization process is segmented into multiple independent objectives: performance optimization and security optimization. Each objective is handled by separate feedback mutators that evaluate and mutate prompts according to their specific criteria, allowing both performance and security to be optimized simultaneously without one compromising the other
Solution Approach 2:
The system changes the evaluation parameters by introducing multiple objective functions with different weights. The pareto selection function evaluates prompts based on multiple parameters including performance metrics and security metrics, selecting prompts that achieve optimal balance across all parameters rather than optimizing for a single parameter
2Adaptability or versatility
If prompts are optimized for flexibility to handle diverse questions, then adaptability is improved, but control over malicious content decreases
Solution Approach 1:
The optimization process is dynamic and iterative, using evolutionary algorithms that continuously mutate and evaluate prompts across multiple generations. The system adapts the prompt population over time based on feedback from multiple objectives, allowing flexible handling of diverse questions while dynamically filtering out malicious content through security-focused evaluation criteria
Solution Approach 2:
Feedback mechanisms are introduced through objective feedback mutators that evaluate prompts against multiple criteria including security constraints. The pareto selection function uses feedback from both performance and security evaluations to guide the evolutionary optimization, ensuring that flexible prompts that handle diverse questions well are selected while those generating malicious content are eliminated
Data Source
AI summary
A method includes performing a semantic mutation on an initial prompt by a prompt optimizer large language model (LLM) to obtain an initial generation of prompts. The method further includes evaluating the initial generation of prompts using a pareto selection function, to obtain a first generation of prompts. The method further includes mutating the first generation of prompts according to a first objective. The method further includes mutating a second generation of prompts obtained from the first generation of prompts according to a second objective. The method further includes performing a crossover mutation on a generation of parent prompts obtained from the second generation of prompts to obtain a result population of prompts. The method further includes adding the result population of prompts to a prompt population.