Embedding-Based Attack Detection for Distributed ML Queries
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Solution Overview
Problem
Machine learning models are vulnerable to distributed adversarial attacks where multiple users coordinate to cause the model to misclassify or steal the model by submitting slightly modified samples, making it difficult to detect and remediate these attacks effectively.
Innovation Solution
A system that analyzes user and query embeddings using distance-based similarity techniques to identify clusters of similar queries and users, generating alerts and initiating remediation actions to prevent or mitigate adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple users submit slightly modified samples in a coordinated fashion, then the machine learning model becomes vulnerable to misclassification or model stealing, but detecting these distributed adversarial attacks becomes difficult
Solution Approach 1:
The patent combines multiple detection dimensions into a unified analysis framework. It merges query embedding similarity analysis, user behavior pattern analysis, and temporal analysis to detect distributed adversarial attacks. By combining these different analytical approaches, the system can identify coordinated attacks that individual methods would miss, thereby improving model security while maintaining detectability.
Solution Approach 2:
The patent introduces an intermediary detection system that acts as a mediator between the machine learning model and incoming queries. This intermediary layer analyzes query embeddings, user behaviors, and temporal patterns before queries reach the model, enabling detection of distributed attacks without directly modifying the model itself. The intermediary system transforms complex attack patterns into detectable signals.
2Measurement precision
If the machine learning model processes all incoming queries to ensure accurate detection, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent segments the detection process into multiple independent analysis components: query embedding similarity analysis, user behavior analysis, and temporal pattern analysis. Each segment can be processed independently and in parallel, reducing overall processing time while maintaining comprehensive detection accuracy. The segmentation allows the system to handle large volumes of queries efficiently.
Solution Approach 2:
The patent implements partial action by using approximate nearest neighbor search and sampling techniques to analyze query embeddings. Instead of exhaustively comparing every query against all historical queries, the system performs partial comparisons that provide sufficient detection accuracy with reduced computational overhead. This approach maintains measurement precision while significantly reducing processing time.
3Measurement precision
If the system stores and analyzes all historical queries and user data, then attack pattern recognition improves, but data storage requirements and system complexity increase
Solution Approach 1:
The patent extracts only the essential features from historical queries and user data for analysis. Instead of storing and processing complete query texts and all user metadata, the system extracts relevant embedding vectors, user behavior metrics, and temporal patterns. This extraction reduces data storage requirements and simplifies the analysis process while maintaining pattern recognition accuracy.
Solution Approach 2:
The patent transforms complex raw data into simplified parameters for analysis. It converts historical queries into embedding vectors, user behaviors into behavioral metrics, and temporal patterns into statistical features. These parameter transformations reduce data dimensionality and complexity while preserving the essential information needed for accurate attack pattern recognition.
Data Source
AI summary
A query to be input into a machine learning model which is associated with a first user is received. A first embedding is generated based on the query. A plurality of historical queries of the machine learning model having a corresponding embedding meeting first criteria relative to the first embedding is then determined using a first distance-based similarity analysis technique. In addition, a plurality of other users of the machine learning model each having a corresponding user embedding meeting second criteria relative to a user embedding for the first user are determined using a second distance-based similarity analysis technique. Data indicating a potential attack on the machine learning model is provided to a consuming application or process based on the query neighbor determination and the user neighbor determination.