Large language model security risk assessment method, system and equipment oriented to long text scene

By constructing a security risk assessment data set for short text and long text scenarios, the security risks of large language models in long text scenarios are evaluated, and the problem of insufficient security guarantee for large models in long text scenarios in the existing technology is solved, and automated evaluation and security performance improvement are achieved.

CN119647621AActive Publication Date: 2025-03-18HARBIN INST OF TECH
8 Cites 1 Cited by

Patent Information

Application Number
CN202411671767.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-21
Publication Date
2025-03-18
Estimated Expiration
2044-11-21

AI Technical Summary

Technical Problem

The existing large language models have serious security guarantees in long text scenarios, and there is a lack of methods to directly evaluate the security risks of large models in long text scenarios.

Method used

By leveraging short text security domain datasets, short text scenario security risk assessment datasets are constructed and extended to long text scenarios, and the harmful long text context datasets and long text security risk assessment datasets are used to evaluate the security risks of large language models in long text scenarios.

Benefits of technology

The security risks of automated evaluation of large models in long text scenarios are realized, which reduces the cost of constructing long context evaluation data. The proposed security evaluation indicators can more accurately reflect the randomness of the security output of large models and improves the security performance of large models.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a long text scene-oriented large language model security risk assessment method, system and equipment, belongs to the technical field of large language models, and solves the problem that the security guarantee of a large language model in a long text scene is seriously insufficient and a great gap of a security assessment method exists. The method comprises the steps that when a large model faces long text input, whether the safety performance of the same harmful task is consistent with an evaluation result in a short text scene or not is evaluated for the same harmful task; according to the method, the short text scene security field data is utilized to automatically generate the harmful content with consistent overall semantics and in a long text form, the long text scene security risk assessment data set is constructed, and the potential security risk of the model in the long text scene is identified. An adopted long text evaluation method comprises context learning, a harmful long text generation technology based on reverse length expansion and a thinking chain, and a safety evaluation index subjected to unbiased estimation processing. The method is suitable for evaluating the security risk of the large language model in the long text scene.
Need to check novelty before this filing date? Find Prior Art