GenAI Traffic Security Inspection via Bidirectional API Classification
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Solution Overview
Problem
Existing cybersecurity solutions are not tailored for Generative Artificial Intelligence (GenAI) applications, leading to high numbers of false positives and false negatives when detecting prompt injection attacks and data leaks.
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 general security coverage is maintained, but detection accuracy deteriorates due to high false positives and false negatives
Solution Approach 1:
The patent changes the detection parameters from general cybersecurity patterns to GenAI-specific patterns including prompt injection techniques, system prompt leakage indicators, and training data extraction signatures. This allows the system to accurately detect GenAI threats while reducing false positives from general-purpose security tools.
Solution Approach 2:
The system creates a specialized detection model that copies and adapts the functionality of general security tools (WAF, IPS, DLP) but tailors it specifically for GenAI applications. This involves replicating security inspection mechanisms while training them on GenAI-specific attack patterns and responses.
2Reliability
If single-direction detection (client-to-server or server-to-client) is used, then system complexity is reduced, but detection completeness deteriorates due to missed bidirectional threats
Solution Approach 1:
The patent segments the detection system into separate inspection modules for client-to-server traffic and server-to-client traffic. Each module independently analyzes its direction using specialized models (request classifier and response classifier), then the results are combined for comprehensive bidirectional security assessment.
Solution Approach 2:
The system implements a universal security inspection framework that handles multiple traffic directions through a single integrated platform. The same infrastructure (processing units, classification models, policy enforcement) serves both client-to-server and server-to-client inspection, achieving multi-functionality without proportionally increasing complexity.
3Measurement precision
If existing security policies are applied to GenAI traffic, then general security coverage is maintained, but detection precision deteriorates due to inability to identify GenAI-specific attack patterns
Solution Approach 1:
The system changes security policy parameters from general cybersecurity rules to GenAI-specific policies that recognize prompt injection patterns, system prompt leakage signatures, and training data extraction attempts. This enables precise identification of GenAI attacks while maintaining alignment with enterprise security objectives.
Solution Approach 2:
The patent implements dynamic security policies that adapt to the specific characteristics of GenAI traffic. The classification models continuously learn from new attack patterns and adjust their detection criteria, allowing the system to maintain high precision as GenAI threat landscapes evolve.
Data Source
AI summary
Disclosed is a cloud-based security system implemented using API notifications provided by a GenAI service or application. The security system provides bidirectional traffic inspection to protect against privacy and security concerns related to the GenAI services. The security system receives notifications of traffic including requests directed to the GenAI service from endpoints as well as the GenAI responses. The security system includes a GenAI request classifier trained to classify prompts as benign, prompt injection attack, or uploaded files. The security system further includes a GenAI response classifier trained to classify responses as normal, leaked system prompt, leaked user uploaded files, or leaked training data. Based on the classification, and optionally other security analysis, the security system may enforce security policies based on both the requests and responses that may include triggering alerts to administrators, deleting data stored by the GenAI service, and the like.