Unicode Injection Detection via Linguistic Pattern Analysis
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Solution Overview
Problem
Current methods lack an effective means to detect Unicode injection in text used by natural language processing tasks, such as text classification, which can lead to errors due to the insertion of Unicode characters that bypass auditing systems or alter text encoding.
Innovation Solution
A computer-implemented method involving training a language model to recognize text that does not conform to human writing habits, using negative and positive samples, and employing optical character recognition to generate feature vectors for comparing text data, thereby identifying potential Unicode injection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Unicode characters are inserted into text to bypass auditing systems or cause model errors, then the text can evade detection or manipulation, but the text no longer conforms to human writing habits and becomes detectable through linguistic analysis
Solution Approach 1:
The patent introduces an intermediary detection mechanism that sits between the text processing system and the input text. This intermediary component analyzes linguistic patterns and writing habits to detect Unicode injections before they reach the main processing system, effectively mediating the harmful effect while maintaining normal operation.
Solution Approach 2:
The patent replaces traditional mechanical character-by-character filtering with a linguistic analysis system that evaluates writing patterns, syntax, and semantic coherence. This substitution allows detection of Unicode injections that bypass traditional character-level audits by analyzing higher-level linguistic structures.
2Measurement precision
If traditional character filtering methods are used to detect Unicode characters, then simple characters can be blocked, but Unicode-encoded characters and similar-looking characters from different languages cannot be detected
Solution Approach 1:
The patent changes the detection parameters from simple character codes to linguistic patterns and writing habits. Instead of detecting Unicode characters based on their encoding values, the system analyzes grammatical correctness, semantic meaning, and stylistic consistency, thereby detecting Unicode injections that use visually similar characters from different languages.
Solution Approach 2:
The patent adds new dimensions to the detection problem by moving from one-dimensional character code analysis to multi-dimensional linguistic analysis. This includes analyzing syntax, semantics, pragmatics, and stylistic patterns, enabling detection of Unicode injections that would be invisible to traditional character-level filters.
3Productivity
If text data is processed without Unicode injection detection, then processing speed is maintained, but errors occur in natural language processing tasks such as text classification
Solution Approach 1:
The patent applies preliminary action by detecting and removing Unicode injections before the text processing begins. The detection system analyzes writing habits and linguistic patterns upfront to identify and filter out malicious Unicode characters, ensuring that only clean text reaches the main processing system, thereby maintaining both speed and accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the detection system continuously monitors processing results and adjusts its detection thresholds accordingly. When Unicode injections are detected, the system provides feedback to refine future detections, improving accuracy over time while maintaining processing efficiency through adaptive detection parameters.
Data Source
AI summary
A computer-implemented method, system and computer program product for detecting Unicode injection in text. A language model is trained to determine if text data (e.g., text fragment) conforms with human writing habits using negative and positive samples. Negative samples include samples of text that are not classified as being suspect for containing Unicode characters. Such negative samples include text written by humans. Positive samples include samples of text that are to be classified as being suspect for containing Unicode characters. Such positive samples may be formed by randomly inserting Unicode characters into the corpus of negative samples. After training the language model, the language model is able to determine whether the received text data (e.g., text fragment) is suspect for containing Unicode characters based on whether the text data conforms with human writing habits.