GenAI Traffic Inspection for Prompt Injection and Data Leak Detection
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Solution Overview
Problem
Existing cybersecurity solutions are inadequate for detecting prompt injection attacks and data leaks in generative artificial intelligence (GenAI) applications, leading to high false positives and negatives, and are limited to single-direction traffic analysis.
Innovation Solution
A network security system uses machine learning classifiers to analyze bi-directional GenAI traffic, classifying requests and responses for potential security and privacy issues, applying security policies and scanning for sensitive information, and adjusting risk scores based on classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing cybersecurity solutions (WAF, IPS, DLP) are used for GenAI traffic inspection, then basic attack detection capability 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 malicious activities while reducing false positives by understanding the unique structure and behavior of GenAI interactions.
Solution Approach 2:
The patent replaces traditional mechanical pattern-matching systems (WAF, IPS) with machine learning-based detection systems that can understand semantic meaning and contextual relationships in GenAI traffic. This substitution enables more accurate detection of sophisticated attacks while reducing false positives through intelligent analysis rather than rigid rule-based filtering.
2Device complexity
If single-direction traffic analysis is used, then system complexity is reduced, but detection completeness deteriorates
Solution Approach 1:
The patent segments the traffic analysis into two distinct directional components: client-to-server request analysis and server-to-client response analysis. Each direction is processed by specialized inspection modules that understand the specific characteristics and threats of that direction, improving overall detection completeness while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent creates a universal inspection framework that handles both request and response traffic through a common architecture. The same security policies, risk scoring mechanisms, and alerting systems are applied universally to both directions of traffic, reducing complexity through reuse while maintaining comprehensive detection coverage.
3Reliability
If comprehensive security scanning is applied to all GenAI traffic, then security coverage is improved, but processing time and system performance deteriorate
Solution Approach 1:
The patent applies partial scanning by focusing security inspection only on suspicious or anomalous traffic patterns rather than uniformly scanning all traffic. Risk scoring mechanisms identify high-risk requests and responses that warrant detailed inspection, while low-risk traffic receives minimal or no scanning, maintaining security coverage for critical threats while preserving processing speed for normal traffic.
Solution Approach 2:
The patent implements continuous risk scoring and monitoring that operates asynchronously with traffic flow. Rather than blocking traffic for comprehensive scanning, the system continuously evaluates traffic in real-time, applying security measures only when thresholds are exceeded, thus maintaining both security coverage and processing throughput through non-intrusive continuous monitoring.
Data Source
AI summary
Disclosed is a cloud-based security system implemented in a forward proxy that provides generative artificial intelligence (GenAI) traffic inspection to protect against security and privacy concerns related to GenAI use for protected endpoints. The security system intercepts requests and determines whether those requests are directed to a GenAI application. The security system includes a GenAI request classifier trained to classify prompts submitted to GenAI applications as one of benign, prompt injection attack, or uploaded files. The security system further includes a GenAI response classifier trained to classify responses from GenAI applications as one of 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 on both the requests and responses that block the traffic, trigger alerts to administrators, and the like to enforce security and privacy protection on bidirectional traffic.