Hierarchical AI Threat Moderation for Real-Time Generative Models
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Solution Overview
Problem
Generative AI models face challenges in efficiently detecting and mitigating threats such as prompt injection, jailbreaking, toxicity, privacy violations, intellectual property infringement, hallucinations, and profanity, which can lead to unintended consequences and societal harm, and existing systems are inefficient and computationally costly.
Innovation Solution
A dynamic threat mitigation system using macro and nano classifiers to identify threat probabilities and selectively mitigate specific sub-types of threats in real-time, reducing computational overhead by dynamically configuring the threat detection model based on data attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive threat detection models are used to detect all types of threats, then threat detection coverage is improved, but computational cost and processing time increase
Solution Approach 1:
The threat detection model is segmented into macro-classifiers for broad threat categories and nano-classifiers for specific threat sub-types. This hierarchical segmentation allows the system to first identify general threat types using computationally efficient macro-classifiers, then apply more resource-intensive nano-classifiers only when necessary, thereby maintaining comprehensive detection coverage while reducing overall computational cost.
Solution Approach 2:
The system applies partial action by not deploying all nano-classifiers simultaneously for every input. Instead, it selectively activates only the relevant nano-classifiers based on the output from macro-classifiers. This approach maintains thorough threat detection where needed while avoiding unnecessary computational expenditure on inputs that don't require full analysis.
2Speed
If real-time threat detection is implemented, then response time is improved, but processing power requirements increase
Solution Approach 1:
The processing pipeline is segmented into two stages: rapid macro-classification for immediate threat assessment and detailed nano-classification only when threats are detected. This segmentation enables real-time response for the majority of inputs through lightweight macro-classifiers, while reserving heavier processing power for cases requiring detailed analysis, thus maintaining fast response times without excessive power consumption.
Solution Approach 2:
The system performs partial processing by applying only the necessary level of analysis to each input in real-time. Most inputs receive quick macro-classification without the full computational burden of nano-classification, enabling real-time processing. Full nano-classification is applied partially only when macro-classifiers indicate potential threats, balancing response speed with processing power requirements.
3Productivity
If dynamic configuration of threat detection models is used, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system dynamically configures the threat detection model by selectively activating nano-classifiers based on macro-classifier outputs and data attributes. This dynamic approach optimizes processing efficiency by adapting the detection pipeline to each input's specific needs. The complexity is managed through automated configuration rules and pre-defined activation criteria, making the dynamic behavior systematic rather than ad-hoc.
Solution Approach 2:
The system performs preliminary action by pre-defining configuration rules and activation criteria for different threat scenarios. This preliminary setup enables efficient runtime decision-making without requiring complex real-time analysis. The dynamic configuration leverages pre-computed knowledge about threat patterns and classifier performance, reducing the operational complexity while maintaining high processing efficiency.
Data Source
AI summary
The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.