GenAI Traffic Security System for Prompt Injection Detection
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Solution Overview
Problem
Existing cybersecurity solutions are inadequate for detecting and mitigating prompt injection attacks and data leaks in generative artificial intelligence (GenAI) applications, leading to privacy and security concerns.
Innovation Solution
A cloud-based network security system that uses machine learning models to classify GenAI requests and responses, applying relevant security policies to prevent malicious activities and data breaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing cybersecurity solutions (WAF, IPS, DLP) are used for GenAI applications, then basic security inspection is provided, but false positives and false negatives increase significantly
Solution Approach 1:
The patent changes the detection parameters from traditional cybersecurity patterns to GenAI-specific parameters including prompt injection patterns, system prompt structures, and training data characteristics. This allows accurate identification of GenAI attacks while reducing false positives by adapting to the unique nature of generative AI traffic
Solution Approach 2:
The patent segments the detection system into specialized components: prompt injection detection, system prompt protection, and training data leak prevention. Each segment uses tailored detection mechanisms specific to its threat type, improving overall detection accuracy while maintaining precision
2Reliability
If existing single-direction detection systems are used, then implementation complexity is reduced, but detection completeness deteriorates as bidirectional threats are missed
Solution Approach 1:
The patent implements a dual-directional detection system that simultaneously inspects both client-to-server requests and server-to-client responses using the same security infrastructure. This multi-functional approach ensures comprehensive threat detection without proportionally increasing system complexity
Solution Approach 2:
The patent inverts the traditional single-direction detection model by implementing bidirectional inspection, analyzing both incoming prompts and outgoing responses. This inversion ensures that threats in either direction are detected, improving detection completeness while using the same technical means
3Productivity
If manual training data generation is used for ML models, then training quality is maintained, but training time and computational resources increase significantly
Solution Approach 1:
The patent implements self-service training data generation where the security system automatically generates, labels, and curates training data using its own detection capabilities and existing threat intelligence. This automated process maintains training quality while dramatically improving training efficiency and reducing computational overhead
Solution Approach 2:
The patent performs preliminary actions by pre-generating training data sets and pre-training classification models before deployment. This advance preparation ensures model accuracy is achieved while training time and resource consumption are optimized through efficient data generation processes
Data Source
AI summary
Disclosed is a training data generation system for generating training data used to train machine learning models to inspect GenAI traffic to identify security and privacy concerns related to GenAI use. The training data generation system is seeded with initial prompts. The initial prompts include benign prompts, prompt injection attacks, and uploaded files. Each initial prompt is submitted to multiple GenAI applications to obtain responses. The corresponding prompts and responses are stored in a training data repository. Variations of the initial prompts are generated using, for example, one of the GenAI applications. Each variation is submitted to each of the GenAI applications as well, and the corresponding prompts and responses are stored. Another machine learning model, regex patterns, a combination, or the like may be used to label the prompts and responses in the training data repository to generate a large training data set quickly and efficiently.