Language Model Risk Detection Using Intent-Technique Separation
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Solution Overview
Problem
Existing language models face scalability and generalization issues in detecting prompt injection risks due to exponential growth in the number of samples needed to learn various risk types, especially when combining malicious intent and attack techniques.
Innovation Solution
A risk detection method that separates intent and technique description content from model input data, performing independent risk detection on each to identify prompt injection risks, using rule engines, small parameter models, and vector retrieval to analyze input and output for improved scalability and timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the language model detects prompt injection risks by learning various risk types, then the detection accuracy is improved, but the number of samples needed grows exponentially
Solution Approach 1:
The patent segments the prompt injection detection into two independent components: intent description content and technique description content. The intent detector analyzes malicious intent separately from the technique detector that analyzes attack techniques. This segmentation allows each detector to be trained independently on smaller datasets, avoiding the exponential growth problem while maintaining comprehensive detection capability.
Solution Approach 2:
The patent introduces an intermediary representation layer that transforms both intent and technique descriptions into a unified risk assessment. The intent description content and technique description content are processed through separate detectors and then combined to produce the final risk detection result, enabling efficient sample utilization while maintaining high detection accuracy.
2Reliability
If the language model combines malicious intent and attack techniques for comprehensive risk detection, then the detection completeness is improved, but the system complexity increases
Solution Approach 1:
The patent divides the risk detection system into separate intent detection and technique detection modules. Each module handles a specific aspect of risk analysis independently, reducing the complexity of processing combined malicious intent and attack techniques simultaneously. The segmented architecture allows for simpler, more maintainable detectors that can be trained and deployed separately.
Solution Approach 2:
The patent creates a universal risk detection framework where both intent description content and technique description content are processed through standardized detection pipelines. The same detection infrastructure can handle various types of prompts and attacks by simply changing the input content, reducing overall system complexity while maintaining comprehensive detection capability.
Data Source
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AI summary
The present disclosure provides a risk detection method and apparatus for a language model, a device, a medium and a product, and the method includes: acquiring model input data of a target language model (S1); determining at least one of intent description content and technique description content from the model input data (S2); performing intent risk detection processing on the intent description content determined to obtain an intent risk detection result (S3), and/or performing technique risk detection processing on the technique description content determined to obtain a technique risk detection result (S4); and determining a risk detection result of the target language model applied to the model input data based on at least one of the intent risk detection result obtained and the technique risk detection result obtained (S5).